---
title: "Language and Linguistics — papers — Elamur Research"
description: "Language and Linguistics: 9,206 papers of the Elamur Research library · page 1 of 93 · 1–25, the most cited first — our summary of each, its…"
canonical: "https://research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/"
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# 🗣️ Language and Linguistics

9,206 papers · page 1 of 93 · 1–25 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/)

Language and Linguistics is a subfield of Arts and Humanities in the OpenAlex classification of science. The topics most of its papers here are on: «Natural Language Processing Techniques», «Topic Modeling», «Syntax, Semantics, Linguistic Variation».

A survey · about 17 minutes to read · every paper named here has its own page in this library

**In this article**

1. [What it is](#a-what-it-is)
2. [History and milestones](#a-history-and-milestones)
3. [Main problems and tasks](#a-main-problems-and-tasks)
4. [Methods and schools](#a-methods-and-schools)
5. [The languages on this shelf](#a-the-languages-on-this-shelf)
6. [Datasets, resources and benchmarks](#a-datasets-resources-and-benchmarks)
7. [Landmark papers in this library](#a-landmark-papers-in-this-library)
8. [People](#a-people)
9. [Institutions and places](#a-institutions-and-places)
10. [Open questions](#a-open-questions)
11. [Neighbouring topics](#a-neighbouring-topics)
12. [Key works in this library](#a-key-works-in-this-library)

Linguistics is the scientific study of human language: how sounds and signs combine into words, how words combine into sentences, how sentences carry meaning, and how people use all of this to inform, persuade, ask and apologise. This shelf gathers the parts of that science that meet the written record and the computer. It holds grammars and treebanks, parsers and morphological analysers, lexicons of senses and frames, theories of discourse and politeness, and a large body of work on particular languages, from English and French to Arabic, Hebrew, Aramaic, Latin and Ancient Greek.

The shelf is deliberately broad. Some works here are classics of theoretical linguistics and the philosophy of language, written as books for human readers. Others are resource papers that describe an annotated corpus, a shared task or a toolkit, and many are experimental papers from computational linguistics that measure how well a program can recover structure that linguists describe. What ties them together is the object of study: language itself, its structure and its use, rather than a particular application.

The papers here span two centuries, from nineteenth-century grammars and editions to the latest work on large language models, but the bulk of the collection is recent. Read as a whole, the shelf tells a story that runs from hand-written grammars through annotated corpora and statistical models to neural networks, while the basic questions — what is a word, what is a sentence, what does it mean, and what is the speaker doing with it — have stayed remarkably stable.

## What it is

Linguistics divides its subject into levels. **Phonetics and phonology** study sounds and sound systems; **morphology** studies the internal structure of words; **syntax** studies how words form phrases and sentences; **semantics** studies literal meaning; **pragmatics** studies meaning in context; and **discourse** studies how sentences connect into texts and conversations. Alongside these levels run fields defined by perspective: historical linguistics, typology, sociolinguistics, psycholinguistics, and the description of individual languages.

This shelf is strongest on the levels from morphology to discourse, and on their computational treatment. It covers [syntax and grammar formalisms](https://md.research.elamur.ai/free/en/topics/syntax-grammar/), [treebanks](https://md.research.elamur.ai/free/en/topics/treebanks/) and [dependency parsing](https://md.research.elamur.ai/free/en/topics/dependency-parsing/), [morphology](https://md.research.elamur.ai/free/en/topics/morphology/) and [tokenization and segmentation](https://md.research.elamur.ai/free/en/topics/tokenization/), [lexical semantics](https://md.research.elamur.ai/free/en/topics/lexical-semantics/) and [word sense disambiguation](https://md.research.elamur.ai/free/en/topics/word-sense-disambiguation/), [semantic role labeling](https://md.research.elamur.ai/free/en/topics/semantic-role-labeling/) and [semantic parsing](https://md.research.elamur.ai/free/en/topics/semantic-parsing/), [discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/), [coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/), [speech act theory](https://md.research.elamur.ai/free/en/topics/speech-act-theory/) and [implicature](https://md.research.elamur.ai/free/en/topics/implicature/). It also covers language-specific work, above all on [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) and [Hebrew and Aramaic](https://md.research.elamur.ai/free/en/topics/hebrew-aramaic/), with smaller collections on [Latin](https://md.research.elamur.ai/free/en/topics/latin/), [Ancient Greek](https://md.research.elamur.ai/free/en/topics/ancient-greek/) and [Akkadian and cuneiform](https://md.research.elamur.ai/free/en/topics/akkadian-cuneiform-nlp/).

The boundary with [natural language processing](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) is porous, and many papers stand on both shelves. A rough rule: a paper belongs here when it is about a linguistic structure or a language (a treebank, a theory of coherence, a morphological analyser for Arabic), and on the NLP shelf when it is mainly about an application or a learning method. [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) is a second close neighbour, because translation is where two languages meet and where many evaluation methods for linguistic output were first developed.

## History and milestones

### Grammars, philology and structure

The oldest items on the shelf are grammars, dictionaries and text editions from the nineteenth and early twentieth centuries. They belong to the tradition of philology, which studied languages through their written texts, especially classical and biblical languages, and which produced comparative grammar and the historical study of [language change](https://md.research.elamur.ai/free/en/topics/language-change/). Twentieth-century structuralism treated a language as a system of contrasts, and from the late 1950s generative grammar recast syntax as the study of the rules that generate the sentences of a language.

### Meaning and use

In the 1960s and 1970s philosophers and linguists turned to how language is used. Austin's idea that utterances are acts was systematised in [Searle's *Speech Acts*](https://md.research.elamur.ai/free/en/papers/searle-1969-speech-acts/) (1969). Grice's theory of conversational implicature, [Lakoff's study of hedges](https://md.research.elamur.ai/free/en/papers/lakoff-1973-hedges-study-meaning-criteria/) (1973) and Brown and Levinson's [theory of politeness](https://md.research.elamur.ai/free/en/papers/brown-1987-politeness-some-universals-language-usage/) (1987) followed. Discourse theory took shape in the same years: [Hobbs's algorithm for pronoun resolution](https://md.research.elamur.ai/free/en/papers/hobbs-1978-resolving-pronoun-references/) (1978), [Rhetorical Structure Theory](https://md.research.elamur.ai/free/en/papers/mann-1988-rhetorical-structure-theory-functional/) (1988), and [centering theory](https://md.research.elamur.ai/free/en/papers/grosz-1995-centering-framework-modeling-local-coherence/), circulated in the 1980s and published in its final form in 1995.

Landmark papers on this shelf: the 12 most cited, in time The 12 most cited papers on this shelf span 2002–2023; the dot grows with the citations.

**The numbers**

| Paper | Citations |
| --- | --- |
| [2002 — Bleu: a Method for Automatic Evaluation of Machine Translation](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/) | 32,134 |
| [2003 — Latent Dirichlet Allocation](https://md.research.elamur.ai/free/en/papers/blei-2003-latent-dirichlet-allocation/) | 28,336 |
| [2004 — ROUGE: A Package for Automatic Evaluation of Summaries](https://md.research.elamur.ai/free/en/papers/lin-2004-rouge-package-automatic-evaluation-summaries/) | 13,438 |
| [2013 — Auto-Encoding Variational Bayes](https://md.research.elamur.ai/free/en/papers/kingma-2013-auto-encoding-variational-bayes/) | 15,018 |
| [2014 — GloVe: Global Vectors for Word Representation](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/) | 38,574 |
| [2014 — Neural Machine Translation by Jointly Learning to Align and Translate](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/) | 19,018 |
| [2014 — Convolutional Neural Networks for Sentence Classification](https://md.research.elamur.ai/free/en/papers/kim-2014-convolutional-neural-networks-sentence-classification/) | 16,269 |
| [2017 — Attention Is All You Need](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/) | 16,593 |
| [2019 — Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/) | 17,026 |
| [2020 — Transformers: State-of-the-Art Natural Language Processing](https://md.research.elamur.ai/free/en/papers/wolf-2020-transformers-state-art-natural-language/) | 15,606 |
| [2020 — BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/) | 12,784 |
| [2023 — GPT-4 Technical Report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/) | 12,732 |

### The corpus turn

Around 1990 the field changed direction. Large machine-readable corpora became available, and researchers began to measure language rather than only reason about it. [Church and Hanks](https://md.research.elamur.ai/free/en/papers/church-1990-word-association-norms-mutual-information/) (1990) used mutual information to find word associations in newswire; [Dunning](https://md.research.elamur.ai/free/en/papers/dunning-1993-accurate-methods-statistics-surprise-coincidence/) (1993) gave a better statistic for rare events; [Gale and Church](https://md.research.elamur.ai/free/en/papers/gale-1993-program-aligning-sentences-bilingual-corpora/) (1993) aligned the sentences of parallel texts. The decisive resource was the [Penn Treebank](https://md.research.elamur.ai/free/en/papers/marcus-1993-building-large-annotated-corpus-english/), released in the early 1990s: a large body of English text with part-of-speech tags and phrase-structure trees. In the lexicon, [WordNet](https://md.research.elamur.ai/free/en/papers/miller-1994-wordnet/) and [FrameNet](https://md.research.elamur.ai/free/en/papers/baker-1998-berkeley-framenet/) organised word meaning into networks of senses and frames.

### Statistical parsing and annotation layers

The late 1990s and 2000s were the era of statistical models trained on annotated corpora. [Collins's head-driven models](https://md.research.elamur.ai/free/en/papers/collins-2003-head-driven-statistical-models-natural/) and [Charniak's parser](https://md.research.elamur.ai/free/en/papers/charniak-2000-maximum-entropy-inspired-parser/) set the standard for English constituency parsing; [Klein and Manning](https://md.research.elamur.ai/free/en/papers/klein-2003-accurate-unlexicalized-parsing/) showed how far an unlexicalised grammar could go. New annotation layers were stacked on the same newswire text: predicate-argument structure in [PropBank](https://md.research.elamur.ai/free/en/papers/palmer-2005-proposition-bank-annotated-corpus-semantic/), discourse relations in the [Penn Discourse TreeBank](https://md.research.elamur.ai/free/en/papers/prasad-2008-penn-discourse-treebank-2-0/), and noun predicates in [NomBank](https://md.research.elamur.ai/free/en/papers/meyers-2004-nombank-project-interim-report/). Dependency grammar returned to the centre with the [CoNLL-X shared task](https://md.research.elamur.ai/free/en/papers/buchholz-2006-conll-x-shared-task-multilingual/) of 2006, which compared parsers for many languages on a common format.

Papers on this shelf by year 9,200 dated papers from 1818 to 2026 (36 before 1966 are not drawn); the busiest year is 2026 with 953, and 65% of them appeared in 2018 or later.

**The numbers**

| Year | Papers |
| --- | --- |
| 2026 | 953 |
| 2025 | 411 |
| 2024 | 647 |
| 2023 | 848 |
| 2022 | 559 |
| 2021 | 674 |
| 2020 | 758 |
| 2019 | 598 |
| 2018 | 533 |
| 2017 | 382 |
| 2016 | 393 |
| 2015 | 235 |
| 2014 | 251 |
| 2013 | 166 |
| 2012 | 201 |
| 2011 | 152 |
| 2010 | 190 |
| 2009 | 115 |
| 2008 | 123 |
| 2007 | 96 |
| 2006 | 116 |
| 2005 | 80 |
| 2004 | 116 |
| 2003 | 69 |
| 2002 | 69 |
| 2001 | 30 |
| 2000 | 49 |
| 1999 | 41 |
| 1998 | 40 |
| 1997 | 37 |
| 1996 | 25 |
| 1995 | 27 |
| 1994 | 40 |
| 1993 | 21 |
| 1992 | 17 |
| 1991 | 17 |
| 1990 | 18 |
| 1989 | 6 |
| 1988 | 15 |
| 1987 | 9 |
| 1986 | 7 |
| 1985 | 2 |
| 1983 | 3 |
| 1982 | 3 |
| 1981 | 3 |
| 1980 | 1 |
| 1979 | 3 |
| 1978 | 1 |
| 1977 | 2 |
| 1976 | 5 |
| 1975 | 2 |
| 1974 | 2 |
| 1973 | 1 |
| 1969 | 2 |
| 1961 | 1 |
| 1931 | 1 |
| 1926 | 1 |
| 1925 | 1 |
| 1924 | 1 |
| 1922 | 1 |
| 1921 | 1 |
| 1920 | 1 |
| 1917 | 2 |
| 1916 | 1 |
| 1914 | 1 |
| 1913 | 1 |
| 1905 | 2 |
| 1904 | 2 |
| 1900 | 1 |
| 1898 | 1 |
| 1894 | 2 |
| 1892 | 2 |
| 1891 | 1 |
| 1887 | 1 |
| 1884 | 1 |
| 1877 | 3 |
| 1875 | 1 |
| 1874 | 2 |
| 1872 | 1 |
| 1869 | 1 |
| 1851 | 1 |
| 1818 | 1 |

### Neural networks and universal annotation

From around 2011, neural networks replaced hand-built features. [Collobert and colleagues](https://md.research.elamur.ai/free/en/papers/collobert-2011-natural-language-processing-almost-scratch/) learned word vectors from raw text and reused them for tagging; [Chen and Manning](https://md.research.elamur.ai/free/en/papers/chen-2014-fast-accurate-dependency-parser-neural/) built a fast neural dependency parser; attention, introduced for translation by [Bahdanau, Cho and Bengio](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/), spread to every task. In parallel, [Universal Dependencies](https://md.research.elamur.ai/free/en/papers/nivre-2016-universal-dependencies-v1-multilingual-treebank/) set one annotation scheme for treebanks in dozens of languages. Since the late 2010s, pretrained [language models](https://md.research.elamur.ai/free/en/topics/language-models/) have become the default tool, and much recent work on this shelf asks what such models know about grammar, meaning and use, and how to build them for languages beyond English.

## Main problems and tasks

The shelf organises naturally around a set of recurring problems, each with its own data and evaluation.

- **Segmentation and tagging.** Splitting text into sentences, words and morphemes, then assigning [parts of speech](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/) and morphological features. In English this is close to solved; in Arabic, Hebrew or Turkish, where a written word may contain a preposition, an article, a stem and a pronoun, it remains a central problem.
- **Syntactic parsing.** Assigning a phrase-structure tree or a dependency tree to a sentence. Parsing is evaluated against treebanks, by bracket F-score for constituents and by attachment score for dependencies.
- **Semantic analysis.** Identifying who did what to whom (semantic role labeling), which sense of a word is meant (word sense disambiguation), and how a whole sentence maps to a meaning representation such as [Abstract Meaning Representation](https://md.research.elamur.ai/free/en/papers/banarescu-2013-abstract-meaning-representation-sembanking/).
- **Lexical knowledge.** Building and evaluating dictionaries, sense inventories, collocation lists and [multiword expressions](https://md.research.elamur.ai/free/en/topics/multiword-expressions/), and measuring similarity between words.
- **Reference and coherence.** Resolving pronouns and other mentions to the entities they denote, identifying discourse relations between clauses, and modelling what makes a text coherent.
- **Pragmatics and dialogue.** Recognising speech acts and dialogue acts, politeness, hedging, irony and sarcasm, and the inferences listeners draw beyond what is said.
- **Language description and documentation.** Building resources for individual languages, especially historical and [low-resource languages](https://md.research.elamur.ai/free/en/topics/low-resource-languages/), and tools that let linguists annotate text, audio and video.
- **Evaluation.** Designing metrics and test sets that measure linguistic output fairly, a problem that runs through every other task and that has its own topic, [evaluation and benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/).

## Methods and schools

### Rule-based and formal approaches

The oldest computational tradition writes grammars by hand. Formalisms such as Head-driven Phrase Structure Grammar (HPSG), Lexical-Functional Grammar, Tree-Adjoining Grammar and Combinatory Categorial Grammar (CCG) give precise accounts of syntax and its link to meaning. Large hand-built grammars, such as the English Resource Grammar, are discussed in work like [Flickinger's paper on efficient typed grammars](https://md.research.elamur.ai/free/en/papers/flickinger-2000-building-more-efficient-grammar/). Formal approaches remain strong where precision and explanation matter, and they underlie many treebanks: [CCGbank](https://md.research.elamur.ai/free/en/papers/hockenmaier-2007-ccgbank-corpus-ccg-derivations-dependency/), for instance, re-expressed the Penn Treebank in CCG, which made [wide-coverage CCG parsing](https://md.research.elamur.ai/free/en/papers/clark-2007-wide-coverage-efficient-statistical-parsing/) possible.

### Statistical and feature-based learning

The statistical school treats linguistic analysis as prediction from annotated examples. Generative models (probabilistic grammars, hidden Markov models) gave way in the 2000s to discriminative ones: maximum-entropy models, conditional random fields, and the [structured perceptron of Collins](https://md.research.elamur.ai/free/en/papers/collins-2002-discriminative-training-methods-hidden-markov/). Dependency parsing split into two families: transition-based parsers that build a tree word by word ([Nivre's algorithm](https://md.research.elamur.ai/free/en/papers/nivre-2003-efficient-algorithm-projective-dependency-parsing/), [MaltParser](https://md.research.elamur.ai/free/en/papers/nivre-2006-maltparser-data-driven-parser-generator/)) and graph-based parsers that search for the best-scoring tree ([McDonald's spanning-tree parser](https://md.research.elamur.ai/free/en/papers/mcdonald-2005-non-projective-dependency-parsing-spanning/)). Reranking ([Charniak and Johnson](https://md.research.elamur.ai/free/en/papers/charniak-2005-coarse-fine-n-best-parsing/)) and self-training ([McClosky and colleagues](https://md.research.elamur.ai/free/en/papers/mcclosky-2006-effective-self-training-parsing/)) squeezed further accuracy from the same data.

### Distributional semantics

A separate line holds that a word's meaning can be read from the company it keeps. Count-based models built vectors from co-occurrence statistics; prediction-based models such as word2vec learned them. [Baroni, Dinu and Kruszewski](https://md.research.elamur.ai/free/en/papers/baroni-2014-don-t-count-predict-systematic/) compared the two systematically and found the predictive vectors better on most benchmarks, while [Levy and Goldberg](https://md.research.elamur.ai/free/en/papers/levy-2014-linguistic-regularities-sparse-explicit-word/) showed that much of their apparent magic could be reproduced with sparse explicit vectors. [SimLex-999](https://md.research.elamur.ai/free/en/papers/hill-2015-simlex-999-evaluating-semantic-models/) sharpened the debate by separating similarity from mere association.

### Neural models

Neural methods replaced manual features with learned representations: recurrent networks ([Kiperwasser and Goldberg's BiLSTM parser](https://md.research.elamur.ai/free/en/papers/kiperwasser-2016-simple-accurate-dependency-parsing-bidirectional/), [stack LSTMs](https://md.research.elamur.ai/free/en/papers/dyer-2015-transition-based-dependency-parsing-stack/)), [biaffine attention](https://md.research.elamur.ai/free/en/papers/dozat-2016-deep-biaffine-attention-neural-dependency/), [self-attentive encoders](https://md.research.elamur.ai/free/en/papers/kitaev-2018-constituency-parsing-self-attentive-encoder/), and finally pretrained transformers such as [SpanBERT](https://md.research.elamur.ai/free/en/papers/joshi-2019-spanbert-improving-pre-training-representing/). Toolkits like [Stanza](https://md.research.elamur.ai/free/en/papers/qi-2020-stanza-python-natural-language-processing/) and [UDPipe](https://md.research.elamur.ai/free/en/papers/straka-2017-tokenizing-pos-tagging-lemmatizing-parsing/) brought full neural pipelines to many languages.

### The main debates

Several debates recur across the shelf. One is about **lexicalisation**: how much parsing accuracy comes from word-specific statistics and how much from structure. Another is about **representation**: phrase structure or dependencies, content words or function words as heads, one universal scheme or language-specific ones. A third concerns **word senses**: whether a fixed inventory of discrete senses is a useful fiction, as argued in Kilgarriff's provocatively titled [«I don't believe in word senses»](https://md.research.elamur.ai/free/en/papers/kilgarriff-1997-word-senses/). The most recent asks whether neural models learn linguistic structure or exploit shallow cues. [McCoy, Pavlick and Linzen](https://md.research.elamur.ai/free/en/papers/mccoy-2019-right-wrong-reasons-diagnosing-syntactic/) and [Jia and Liang](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/) built test sets that expose such shortcuts, and [Manning's essay on tagging](https://md.research.elamur.ai/free/en/papers/manning-2011-part-speech-tagging-97-100/) argued that the last errors require linguistics, not just more data.

## The languages on this shelf

Much of the shelf is organised by language rather than by method, and the languages differ in what makes them hard.

### English and the other major European languages

English is the reference point of the field: its first large treebank, its sense network and most of its benchmarks were built for it, and reference works such as [The Cambridge Grammar of the English Language](https://md.research.elamur.ai/free/en/papers/huddleston-2002-cambridge-grammar-english-language/) still anchor descriptive claims. Work on English here ranges from [converting constituent trees into dependencies](https://md.research.elamur.ai/free/en/papers/johansson-2007-extended-constituent-dependency-conversion-english/) to [lexicons of discourse connectives](https://md.research.elamur.ai/free/en/papers/das-2018-constructing-lexicon-english-discourse-connectives/) and [irony in social media](https://md.research.elamur.ai/free/en/papers/hee-2018-semeval-2018-task-3-irony/). French appears through resources such as [CamemBERT](https://md.research.elamur.ai/free/en/papers/martin-2019-camembert-tasty-french-language-model/), a pretrained model built for French, and comparative studies such as [concession in English and German](https://md.research.elamur.ai/free/en/papers/grote-1995-ma-r-king-concessions-english/). Mova's paper cards on English stand on their own [English topic](https://md.research.elamur.ai/free/en/topics/english/).

### Semitic languages

The Semitic family is the largest language-specific collection here. Its words are built from consonantal roots placed into vowel patterns, and its writing systems usually leave out short vowels, so one spelling may correspond to several different words. Arabic work concentrates on [segmentation, tagging and disambiguation](https://md.research.elamur.ai/free/en/papers/habash-2005-arabic-tokenization-part-speech-tagging/), [diacritisation and lemmatisation](https://md.research.elamur.ai/free/en/papers/roth-2008-arabic-morphological-tagging-diacritization-lemmatization/) and the gap between Modern Standard Arabic and the spoken dialects. Hebrew work spans Biblical, Rabbinic and Modern Hebrew, from [joint morphological and syntactic disambiguation](https://md.research.elamur.ai/free/en/papers/tsarfaty-2006-integrated-morphological-syntactic-disambiguation-modern/) to lexical semantics of the Bible, as in a [study of two verbs for boiling in Biblical Hebrew and the Septuagint](https://md.research.elamur.ai/free/en/papers/mangum-2022-bashal-hepso-really-mean-boil/). Aramaic, Ugaritic and Akkadian bring the oldest written records of the family, with questions of [genealogical subgrouping](https://md.research.elamur.ai/free/en/papers/kaye-2026-ugaritic-go-arabic-semitic-genealogical/) and [the vocabulary of Ugaritic texts](https://md.research.elamur.ai/free/en/papers/watson-2026-hair-heel-ugaritic-terms-parts/). The topics [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) and [Hebrew and Aramaic](https://md.research.elamur.ai/free/en/topics/hebrew-aramaic/) survey them in depth, and Mova's cards have shelves for [Biblical Hebrew](https://md.research.elamur.ai/free/en/topics/biblical-hebrew/), [Hebrew](https://md.research.elamur.ai/free/en/topics/modern-hebrew/), [Aramaic](https://md.research.elamur.ai/free/en/topics/aramaic/) and the [Semitic family](https://md.research.elamur.ai/free/en/topics/semitic-languages/).

### Classical and ancient languages

Latin, Ancient Greek and the cuneiform languages share a set of problems: a closed and often damaged corpus, rich inflection, and no native speakers to consult. Work here includes [finite-state analysis of Ancient Greek and Ancient Hebrew stress](https://md.research.elamur.ai/free/en/papers/swanson-2022-handling-stress-finite-state-morphological/), [sentence alignment between Latin and Greek](https://md.research.elamur.ai/free/en/papers/reichbauer-2026-evaluating-latin-ancient-greek-sentence/), [restoring Latin legal manuscripts](https://md.research.elamur.ai/free/en/papers/zhang-2026-language-models-restoration-latin-legal/), an [Akkadian treebank of Neo-Assyrian royal inscriptions](https://md.research.elamur.ai/free/en/papers/luukko-2020-akkadian-treebank-early-neo-assyrian/), [filling gaps in broken Akkadian tablets](https://md.research.elamur.ai/free/en/papers/lazar-2021-filling-gaps-ancient-akkadian-texts/) and the [EvaCun shared task](https://md.research.elamur.ai/free/en/papers/gordin-2025-evacun-2025-shared-task-lemmatization/) on Akkadian and Sumerian lemmatisation. The topics [Latin](https://md.research.elamur.ai/free/en/topics/latin/), [Ancient Greek](https://md.research.elamur.ai/free/en/topics/ancient-greek/) and [Akkadian and cuneiform](https://md.research.elamur.ai/free/en/topics/akkadian-cuneiform-nlp/) collect them.

## Datasets, resources and benchmarks

The field's progress has been driven by shared resources. The most important ones represented here are:

| Resource | What it provides |
| --- | --- |
| [Penn Treebank](https://md.research.elamur.ai/free/en/papers/marcus-1993-building-large-annotated-corpus-english/) | English newswire with part-of-speech tags and phrase-structure trees; the benchmark for English parsing |
| [PropBank](https://md.research.elamur.ai/free/en/papers/palmer-2005-proposition-bank-annotated-corpus-semantic/) | Verb-specific semantic roles over the Penn Treebank |
| [FrameNet](https://md.research.elamur.ai/free/en/papers/baker-1998-berkeley-framenet/) | A lexicon of semantic frames with annotated example sentences |
| [WordNet](https://md.research.elamur.ai/free/en/papers/miller-1994-wordnet/) | A network of English synonym sets linked by semantic relations |
| [Penn Discourse TreeBank](https://md.research.elamur.ai/free/en/papers/prasad-2008-penn-discourse-treebank-2-0/) | Discourse connectives and relations over the same newswire |
| [RST Discourse Treebank](https://md.research.elamur.ai/free/en/papers/carlson-2001-building-discourse-tagged-corpus-framework/) | Text-level discourse trees in Rhetorical Structure Theory |
| [AMR Sembank](https://md.research.elamur.ai/free/en/papers/banarescu-2013-abstract-meaning-representation-sembanking/) | Whole-sentence meaning graphs |
| [Universal Dependencies](https://md.research.elamur.ai/free/en/papers/marneffe-2021-universal-dependencies-2/) | Cross-linguistically consistent treebanks for many languages |
| [OntoNotes coreference](https://md.research.elamur.ai/free/en/papers/pradhan-2012-conll-2012-shared-task-modeling/) | Multilingual coreference used in the CoNLL-2011 and 2012 shared tasks |
| [ACE](https://md.research.elamur.ai/free/en/papers/doddington-2004-automatic-content-extraction-ace-program/) | Entities, relations and events in English, Chinese and Arabic |
| [SentiWordNet](https://md.research.elamur.ai/free/en/papers/baccianella-2010-sentiwordnet-3-0-enhanced-lexical/) | Graded sentiment scores for WordNet synsets |

Shared tasks have played a similar role: [CoNLL-X](https://md.research.elamur.ai/free/en/papers/buchholz-2006-conll-x-shared-task-multilingual/) and the [CoNLL 2017 task](https://md.research.elamur.ai/free/en/papers/zeman-2017-conll-2017-shared-task-multilingual/) for multilingual parsing, [CoNLL-2005](https://md.research.elamur.ai/free/en/papers/carreras-2005-introduction-conll-2005-shared-task/) and [CoNLL-2009](https://md.research.elamur.ai/free/en/papers/hajic-2009-conll-2009-shared-task-syntactic/) for semantic roles, the [SPMRL 2013 task](https://md.research.elamur.ai/free/en/papers/seddah-2013-overview-spmrl-2013-shared-task/) for morphologically rich languages, the [DISRPT tasks](https://md.research.elamur.ai/free/en/papers/zeldes-2021-disrpt-2021-shared-task-elementary/) for discourse segmentation, and the yearly WMT findings for translation. Annotation tools such as [ELAN](https://md.research.elamur.ai/free/en/papers/wittenburg-2006-elan-professional-framework-multimodality-research/) and [rstWeb](https://md.research.elamur.ai/free/en/papers/zeldes-2016-rstweb-browser-based-annotation-interface/) support the human work behind all of these.

## Landmark papers in this library

Citations of the papers on this shelf, by when they were published Papers published in 2015–2019 gather 32% of the 2,295,828 citations of the papers on this shelf.

**The numbers**

| Published | Papers and their citations |
| --- | --- |
| 2025–2029 | 1,364 papers, 24,065 citations |
| 2020–2024 | 3,486 papers, 700,671 citations |
| 2015–2019 | 2,141 papers, 729,144 citations |
| 2010–2014 | 960 papers, 313,174 citations |
| 2005–2009 | 530 papers, 167,974 citations |
| 2000–2004 | 333 papers, 214,590 citations |
| 1995–1999 | 170 papers, 73,569 citations |
| 1990–1994 | 113 papers, 40,247 citations |
| 1985–1989 | 39 papers, 22,363 citations |
| 1980–1984 | 10 papers, 1,073 citations |
| 1975–1979 | 13 papers, 754 citations |
| 1970–1974 | 3 papers, 1,486 citations |
| 1965–1969 | 2 papers, 6,673 citations |
| 1960–1964 | 1 papers, 4 citations |
| 1930–1934 | 1 papers, 0 citations |
| 1925–1929 | 2 papers, 0 citations |
| 1920–1924 | 4 papers, 0 citations |
| 1915–1919 | 3 papers, 0 citations |
| 1910–1914 | 2 papers, 0 citations |
| 1905–1909 | 2 papers, 0 citations |
| 1900–1904 | 3 papers, 0 citations |
| 1895–1899 | 1 papers, 0 citations |
| 1890–1894 | 5 papers, 0 citations |
| 1885–1889 | 1 papers, 0 citations |
| 1880–1884 | 1 papers, 0 citations |
| 1875–1879 | 4 papers, 0 citations |
| 1870–1874 | 3 papers, 0 citations |
| 1865–1869 | 1 papers, 0 citations |
| 1850–1854 | 1 papers, 0 citations |
| 1815–1819 | 1 papers, 0 citations |

### BLEU (2002)

[Papineni and colleagues](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/) proposed an automatic translation score built from clipped n-gram precision against human references, with a penalty for output that is too short. It is the most cited work on the shelf because it made rapid, repeatable evaluation possible. Its weaknesses at sentence level led to [METEOR](https://md.research.elamur.ai/free/en/papers/banerjee-2005-meteor-automatic-metric-mt-evaluation/), [TER](https://md.research.elamur.ai/free/en/papers/snover-2006-study-translation-edit-rate-targeted/), [chrF](https://md.research.elamur.ai/free/en/papers/popovic-2015-chrf-character-n-gram-f/) and the learned metric [COMET](https://md.research.elamur.ai/free/en/papers/rei-2020-comet-neural-framework-mt-evaluation/).

### Speech Acts (1969)

[Searle's book](https://md.research.elamur.ai/free/en/papers/searle-1969-speech-acts/) treated speaking as rule-governed action and analysed the conditions under which a promise, a request or an assertion succeeds. Its notion of devices that signal illocutionary force became the basis for later work on dialogue acts, such as the [Switchboard dialogue act model](https://md.research.elamur.ai/free/en/papers/stolcke-2000-dialogue-act-modeling-automatic-tagging/) of Stolcke and colleagues.

### Politeness (1987)

[Brown and Levinson](https://md.research.elamur.ai/free/en/papers/brown-1987-politeness-some-universals-language-usage/) explained polite language as the management of "face", a speaker's wish to be approved of and to be left unimpeded. Comparing English, Tzeltal and Tamil, they argued that the strategies are universal. The book remains the standard reference in pragmatics and in computational studies of politeness, including [control of politeness in neural translation](https://md.research.elamur.ai/free/en/papers/sennrich-2016-controlling-politeness-neural-machine-translation/).

### Rhetorical Structure Theory (1988)

[Mann and Thompson](https://md.research.elamur.ai/free/en/papers/mann-1988-rhetorical-structure-theory-functional/) analysed texts as trees of spans joined by relations such as Evidence, Concession and Elaboration, most of them pairing a central nucleus with a supporting satellite. RST became one of the main frameworks for discourse parsing and led to the [RST Discourse Treebank](https://md.research.elamur.ai/free/en/papers/carlson-2001-building-discourse-tagged-corpus-framework/) and to [sentence-level discourse parsers](https://md.research.elamur.ai/free/en/papers/soricut-2003-sentence-level-discourse-parsing-syntactic/).

### The Penn Treebank (1993)

[Marcus, Santorini and Marcinkiewicz](https://md.research.elamur.ai/free/en/papers/marcus-1993-building-large-annotated-corpus-english/) described how millions of words of English were tagged and bracketed by correcting automatic output rather than annotating from scratch. Almost every later parser for English was trained and tested on this resource, and it was extended with [predicate-argument structure](https://md.research.elamur.ai/free/en/papers/marcus-1994-penn-treebank-annotating-predicate-argument/), PropBank and the Penn Discourse TreeBank.

### WordNet (1994) and FrameNet (1998)

[WordNet](https://md.research.elamur.ai/free/en/papers/miller-1994-wordnet/) grouped English words into synonym sets connected by hyponymy, meronymy and other relations, and became the default sense inventory for English. [FrameNet](https://md.research.elamur.ai/free/en/papers/baker-1998-berkeley-framenet/), led by Charles Fillmore, described words by the situations they evoke and the roles in them. FrameNet made possible the first statistical [semantic role labeler](https://md.research.elamur.ai/free/en/papers/gildea-2002-automatic-labeling-semantic-roles/) of Gildea and Jurafsky, and WordNet became the base for affective resources such as [WordNet-Affect](https://md.research.elamur.ai/free/en/papers/strapparava-2004-wordnet-affect-affective-extension-wordnet/) and [SentiWordNet](https://md.research.elamur.ai/free/en/papers/esuli-2006-sentiwordnet-publicly-available-lexical-resource/).

### Centering (1995)

[Grosz, Joshi and Weinstein](https://md.research.elamur.ai/free/en/papers/grosz-1995-centering-framework-modeling-local-coherence/) modelled local coherence through the entities each utterance looks back to and looks forward to, and through rules linking pronouns to the most salient entity. Centering shaped pronoun resolution and coherence modelling for two decades and was tested on many languages.

### Head-driven and unlexicalised parsing (2000–2003)

[Collins](https://md.research.elamur.ai/free/en/papers/collins-2003-head-driven-statistical-models-natural/) generated trees outward from their heads with probabilities conditioned on head words, distance and subcategorisation; [Charniak](https://md.research.elamur.ai/free/en/papers/charniak-2000-maximum-entropy-inspired-parser/) pushed accuracy higher with a smoothing scheme inspired by log-linear models. [Klein and Manning](https://md.research.elamur.ai/free/en/papers/klein-2003-accurate-unlexicalized-parsing/) then showed that careful structural splits of grammar categories, with no word statistics at all, could rival early lexicalised parsers — a result later automated by [Petrov and colleagues](https://md.research.elamur.ai/free/en/papers/petrov-2006-learning-accurate-compact-interpretable-tree/).

### PropBank (2005)

[Palmer, Gildea and Kingsbury](https://md.research.elamur.ai/free/en/papers/palmer-2005-proposition-bank-annotated-corpus-semantic/) labelled the arguments of every verb in the Penn Treebank newswire with numbered roles defined per verb sense. Semantic role labelers have been trained on this corpus ever since, and its role sets were later reused as the predicates of AMR.

### Stanford typed dependencies (2006)

[De Marneffe, MacCartney and Manning](https://md.research.elamur.ai/free/en/papers/marneffe-2006-generating-typed-dependency-parses-phrase/) converted phrase-structure trees into labelled grammatical relations that favour content words as heads. The scheme, refined in [a 2008 paper](https://md.research.elamur.ai/free/en/papers/marneffe-2008-stanford-typed-dependencies-representation/) and generalised in [Universal Stanford Dependencies](https://md.research.elamur.ai/free/en/papers/marneffe-2014-universal-stanford-dependencies-cross-linguistic/), became the direct ancestor of Universal Dependencies.

### Neural NLP from scratch (2011)

[Collobert and colleagues](https://md.research.elamur.ai/free/en/papers/collobert-2011-natural-language-processing-almost-scratch/) trained one neural architecture for tagging, chunking, named entities and semantic roles, using word vectors learned from unlabelled text instead of engineered features. With [Turian, Ratinov and Bengio's comparison of word representations](https://md.research.elamur.ai/free/en/papers/turian-2010-word-representations-simple-general-method/), it set the stage for embeddings throughout the field.

### Abstract Meaning Representation (2013)

[Banarescu and colleagues](https://md.research.elamur.ai/free/en/papers/banarescu-2013-abstract-meaning-representation-sembanking/) proposed a single graph for the meaning of a whole sentence, abstracting away from syntax so that paraphrases receive the same graph. AMR created a new task, [semantic parsing into graphs](https://md.research.elamur.ai/free/en/papers/konstas-2017-neural-amr-sequence-sequence-models/), and a new kind of semantic treebank.

### Stanford CoreNLP (2014)

[Manning and colleagues](https://md.research.elamur.ai/free/en/papers/manning-2014-stanford-corenlp-natural-language-processing/) described a deliberately simple pipeline that chains tokenisation, tagging, parsing, named entities and coreference. Its wide adoption made linguistic analysis an ordinary tool for researchers outside linguistics, a role later taken up by [Stanza](https://md.research.elamur.ai/free/en/papers/qi-2020-stanza-python-natural-language-processing/) and [AllenNLP](https://md.research.elamur.ai/free/en/papers/gardner-2018-allennlp-deep-semantic-natural-language/).

### Universal Dependencies (2016)

[Nivre and colleagues](https://md.research.elamur.ai/free/en/papers/nivre-2016-universal-dependencies-v1-multilingual-treebank/) defined one inventory of part-of-speech tags, morphological features and dependency relations for all languages, with function words attached to the content words they serve. UD grew into the largest multilingual collection of treebanks; its later state is described in [Universal Dependencies v2](https://md.research.elamur.ai/free/en/papers/nivre-2020-universal-dependencies-v2-evergrowing-multilingual/) and in the [2021 overview](https://md.research.elamur.ai/free/en/papers/marneffe-2021-universal-dependencies-2/), and it has its own topic, [Universal Dependencies](https://md.research.elamur.ai/free/en/topics/universal-dependencies/).

## People

Authors with the most papers on this shelf Nizar Habash leads with 87 papers; the 12 authors shown wrote 719 of the papers on this shelf between them, counting each paper once per author.

**The numbers**

| Author | Papers here · their citations |
| --- | --- |
| [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/) | 87 papers · 7,899 citations |
| [Christopher Manning](https://md.research.elamur.ai/free/en/authors/christopher-d-manning/) | 82 papers · 114,536 citations |
| [Luke Zettlemoyer](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/) | 70 papers · 42,066 citations |
| [Noah A. Smith](https://md.research.elamur.ai/free/en/authors/noah-a-smith/) | 68 papers · 18,582 citations |
| [Graham Neubig](https://md.research.elamur.ai/free/en/authors/graham-neubig/) | 61 papers · 20,828 citations |
| [Iryna Gurevych](https://md.research.elamur.ai/free/en/authors/iryna-gurevych/) | 60 papers · 31,907 citations |
| [Yejin Choi](https://md.research.elamur.ai/free/en/authors/yejin-choi/) | 54 papers · 16,755 citations |
| [Massimo Poesio](https://md.research.elamur.ai/free/en/authors/massimo-poesio/) | 53 papers · 6,121 citations |
| [Yoav Goldberg](https://md.research.elamur.ai/free/en/authors/yoav-goldberg/) | 49 papers · 15,674 citations |
| [Hannaneh Hajishirzi](https://md.research.elamur.ai/free/en/authors/hannaneh-hajishirzi/) | 46 papers · 16,270 citations |
| [Dan Roth](https://md.research.elamur.ai/free/en/authors/dan-roth/) | 46 papers · 12,891 citations |
| [Philipp Koehn](https://md.research.elamur.ai/free/en/authors/philipp-koehn/) | 43 papers · 18,820 citations |

- [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/) (New York University Abu Dhabi) is the most prolific author on this shelf. His work on Arabic runs from [joint tokenisation, tagging and disambiguation](https://md.research.elamur.ai/free/en/papers/habash-2005-arabic-tokenization-part-speech-tagging/) through the [MADAMIRA](https://md.research.elamur.ai/free/en/papers/pasha-2014-madamira-fast-comprehensive-tool-morphological/) analyser to the open [CAMeL Tools](https://md.research.elamur.ai/free/en/papers/obeid-2020-camel-tools-open-source-python/), and includes a [conventional orthography for dialectal Arabic](https://md.research.elamur.ai/free/en/papers/habash-2012-conventional-orthography-dialectal-arabic/).
- [Owen Rambow](https://md.research.elamur.ai/free/en/authors/owen-rambow/) (Stony Brook University) co-authored much of the same Arabic morphology work, including [morphological tagging, diacritisation and lemmatisation](https://md.research.elamur.ai/free/en/papers/roth-2008-arabic-morphological-tagging-diacritization-lemmatization/).
- [Massimo Poesio](https://md.research.elamur.ai/free/en/authors/massimo-poesio/) (Queen Mary University of London) has shaped anaphora annotation and resolution, from the [ARRAU corpus](https://md.research.elamur.ai/free/en/papers/poesio-2008-anaphoric-annotation-arrau-corpus/) to [bridging references](https://md.research.elamur.ai/free/en/papers/poesio-2004-learning-resolve-bridging-references/) and the [BART toolkit](https://md.research.elamur.ai/free/en/papers/versley-2008-bart-modular-toolkit-coreference-resolution/).
- [Christopher Manning](https://md.research.elamur.ai/free/en/authors/christopher-d-manning/) (Stanford University) is among the most cited authors here, with work on [unlexicalised parsing](https://md.research.elamur.ai/free/en/papers/klein-2003-accurate-unlexicalized-parsing/), [typed dependencies](https://md.research.elamur.ai/free/en/papers/marneffe-2006-generating-typed-dependency-parses-phrase/), [neural dependency parsing](https://md.research.elamur.ai/free/en/papers/chen-2014-fast-accurate-dependency-parser-neural/) and the CoreNLP and Stanza toolkits.
- [Joakim Nivre](https://md.research.elamur.ai/free/en/authors/joakim-nivre/) (Uppsala University) introduced [efficient transition-based dependency parsing](https://md.research.elamur.ai/free/en/papers/nivre-2003-efficient-algorithm-projective-dependency-parsing/), co-organised the [CoNLL 2008 shared task](https://md.research.elamur.ai/free/en/papers/surdeanu-2008-conll-2008-shared-task-joint/), and led Universal Dependencies.
- [Marie-Catherine de Marneffe](https://md.research.elamur.ai/free/en/authors/marie-catherine-de-marneffe/) (UCLouvain) designed the Stanford dependency scheme and co-founded Universal Dependencies.
- [Daniel Zeman](https://md.research.elamur.ai/free/en/authors/daniel-zeman/) (Charles University) co-maintains Universal Dependencies and organised the [CoNLL 2017 parsing task](https://md.research.elamur.ai/free/en/papers/zeman-2017-conll-2017-shared-task-multilingual/).
- [Reut Tsarfaty](https://md.research.elamur.ai/free/en/authors/reut-tsarfaty/) (Bar-Ilan University) works on parsing morphologically rich languages, especially Hebrew, from [joint morphological and syntactic disambiguation](https://md.research.elamur.ai/free/en/papers/tsarfaty-2006-integrated-morphological-syntactic-disambiguation-modern/) to the [SPMRL shared task](https://md.research.elamur.ai/free/en/papers/seddah-2013-overview-spmrl-2013-shared-task/).
- [Avi Shmidman](https://md.research.elamur.ai/free/en/authors/avi-shmidman/) (Bar-Ilan University) builds tools for Hebrew, including the [Nakdan diacritiser](https://md.research.elamur.ai/free/en/papers/shmidman-2020-nakdan-professional-hebrew-diacritizer/), [DictaBERT](https://md.research.elamur.ai/free/en/papers/shmidman-2023-dictabert-state-art-bert-suite/) and [DictaLM 2.0](https://md.research.elamur.ai/free/en/papers/shmidman-2024-adapting-llms-hebrew-unveiling-dictalm/).
- [Martha Palmer](https://md.research.elamur.ai/free/en/authors/martha-palmer/) led [PropBank](https://md.research.elamur.ai/free/en/papers/palmer-2005-proposition-bank-annotated-corpus-semantic/) and its precursor [From TreeBank to PropBank](https://md.research.elamur.ai/free/en/papers/kingsbury-2002-treebank-propbank/), and took part in AMR and the OntoNotes coreference tasks.
- [Bonnie Webber](https://md.research.elamur.ai/free/en/authors/bonnie-webber/) (University of Edinburgh) and [Aravind Joshi](https://md.research.elamur.ai/free/en/authors/aravind-joshi/) developed the [Penn Discourse Treebank](https://md.research.elamur.ai/free/en/papers/miltsakaki-2004-penn-discourse-treebank/) and its theory of [anaphora and discourse structure](https://md.research.elamur.ai/free/en/papers/webber-2003-anaphora-discourse-structure/); Joshi is also a co-author of centering.
- [Michael Strube](https://md.research.elamur.ai/free/en/authors/michael-strube/) (Heidelberg Institute for Theoretical Studies) works on coreference, from [using WordNet and Wikipedia as knowledge](https://md.research.elamur.ai/free/en/papers/ponzetto-2006-exploiting-semantic-role-labeling-wordnet/) to [scoring coreference partitions](https://md.research.elamur.ai/free/en/papers/pradhan-2014-scoring-coreference-partitions-predicted-mentions/).
- [Vincent Ng](https://md.research.elamur.ai/free/en/authors/vincent-ng/) improved [learning-based coreference](https://md.research.elamur.ai/free/en/papers/ng-2002-improving-machine-learning-approaches-coreference/), surveyed [fifteen years of the task](https://md.research.elamur.ai/free/en/papers/ng-2010-supervised-noun-phrase-coreference-research/) and studied [hard Winograd-style pronouns](https://md.research.elamur.ai/free/en/papers/rahman-2012-resolving-complex-cases-definite-pronouns/).
- [Amir Zeldes](https://md.research.elamur.ai/free/en/authors/amir-zeldes/) and [Nathan Schneider](https://md.research.elamur.ai/free/en/authors/nathan-schneider/) (both Georgetown University) work on discourse annotation and lexical semantics respectively; Zeldes co-created [CorefUD](https://md.research.elamur.ai/free/en/papers/nedoluzhko-2022-corefud-1-0-coreference-meets/), and Schneider co-authored AMR and [frame-semantic parsing](https://md.research.elamur.ai/free/en/papers/das-2014-frame-semantic-parsing/).
- [Dan Klein](https://md.research.elamur.ai/free/en/authors/dan-klein/) (University of California, Berkeley) contributed unlexicalised and [latent-annotation parsing](https://md.research.elamur.ai/free/en/papers/petrov-2006-learning-accurate-compact-interpretable-tree/) and [self-attentive constituency parsing](https://md.research.elamur.ai/free/en/papers/kitaev-2018-constituency-parsing-self-attentive-encoder/).
- [Manfred Stede](https://md.research.elamur.ai/free/en/authors/manfred-stede/) (University of Potsdam) and [Vera Demberg](https://md.research.elamur.ai/free/en/authors/vera-demberg/) (Saarland University) study discourse connectives and relations, from the [DiMLex lexicon](https://md.research.elamur.ai/free/en/papers/stede-1998-dimlex-lexicon-discourse-markers-text/) to [implicit relations](https://md.research.elamur.ai/free/en/papers/asr-2012-implicitness-discourse-relations/).

## Institutions and places

The authors on this shelf cluster in a few kinds of places. Large American research universities — Carnegie Mellon, Johns Hopkins, Stanford, New York University, the University of Washington and Georgetown — are prominent, alongside industrial laboratories such as Google, Amazon and the Allen Institute for Artificial Intelligence. In Europe, the University of Edinburgh, Charles University in Prague (home of the Prague Dependency Treebank tradition and much of the Universal Dependencies infrastructure), Saarland University, Utrecht University and the French CNRS stand out.

Where the authors on this shelf work now Carnegie Mellon University is the current home of authors of 538 papers on this shelf; counted are the authors with a page here, by their latest institution.

**The numbers**

| Institution | Papers |
| --- | --- |
| [Carnegie Mellon University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) | 538 papers |
| [Google](https://md.research.elamur.ai/free/en/companies/google/) | 500 papers |
| [Stanford University](https://md.research.elamur.ai/free/en/universities/stanford-university/) | 483 papers |
| [University of Washington](https://md.research.elamur.ai/free/en/universities/university-of-washington/) | 378 papers |
| [Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/) | 359 papers |
| [Tsinghua University](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) | 316 papers |
| [New York University](https://md.research.elamur.ai/free/en/universities/new-york-university/) | 280 papers |
| [Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) | 266 papers |
| [University of Edinburgh](https://md.research.elamur.ai/free/en/universities/university-of-edinburgh/) | 263 papers |
| [Google DeepMind](https://md.research.elamur.ai/free/en/companies/google-deepmind/) | 247 papers |

A distinctive feature of this shelf is the weight of the Middle East and the Gulf, which reflects its strength in Semitic languages. New York University Abu Dhabi and the Mohamed bin Zayed University of Artificial Intelligence in the United Arab Emirates, Hamad bin Khalifa University in Qatar, and Bar-Ilan University in Israel are among the leading homes of authors here, working respectively on Arabic and on Hebrew.

Countries of the authors on this shelf United States comes first: authors working there now wrote 4,127 papers on this shelf.

**The numbers**

| Country | Papers |
| --- | --- |
| [United States](https://md.research.elamur.ai/free/en/countries/united-states/) | 4,127 papers |
| [China](https://md.research.elamur.ai/free/en/countries/china/) | 1,816 papers |
| [United Kingdom](https://md.research.elamur.ai/free/en/countries/united-kingdom/) | 1,253 papers |
| [Germany](https://md.research.elamur.ai/free/en/countries/germany/) | 799 papers |
| [Canada](https://md.research.elamur.ai/free/en/countries/canada/) | 419 papers |
| Hong Kong | 402 papers |
| [Israel](https://md.research.elamur.ai/free/en/countries/israel/) | 381 papers |
| [France](https://md.research.elamur.ai/free/en/countries/france/) | 359 papers |
| [United Arab Emirates](https://md.research.elamur.ai/free/en/countries/united-arab-emirates/) | 338 papers |
| [Netherlands](https://md.research.elamur.ai/free/en/countries/netherlands/) | 320 papers |

The venues show the field's dependence on shared resources. The Language Resources and Evaluation Conference (LREC) carries more papers on this shelf than any other venue, followed by the journal *Computational Linguistics*, *Glossa*, *Transactions of the ACL* and the annual ACL meetings.

Where the papers on this shelf were published Annual Meeting of the Association for Computational Linguistics (ACL) carries the most, 1265 of 9206.

**The numbers**

| Venue | Papers |
| --- | --- |
| [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) | 1,265 papers |
| [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) | 895 papers |
| [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/) | 813 papers |
| [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/) | 425 papers |
| [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/) | 318 papers |
| [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/) | 279 papers |
| [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/) | 206 papers |
| [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/) | 177 papers |
| [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/) | 155 papers |
| [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/) | 144 papers |

## Open questions

- **What do language models know about grammar?** Pretrained models parse and tag well, but it is still unclear how far they represent syntax, and whether they generalise as humans do or rely on [shallow heuristics](https://md.research.elamur.ai/free/en/papers/mccoy-2019-right-wrong-reasons-diagnosing-syntactic/). This connects to [interpretability](https://md.research.elamur.ai/free/en/topics/interpretability/) and [psycholinguistics](https://md.research.elamur.ai/free/en/topics/psycholinguistics/).
- **Morphologically rich and low-resource languages.** Most tools still work best for English. Arabic dialects, Hebrew, Amharic and many others have rich morphology, little annotated data and often non-standard spelling, and historical languages add damaged and undeciphered texts.
- **Meaning beyond the sentence.** Coreference, discourse relations and implicature still lag behind syntax. Implicit discourse relations, bridging anaphora and [inherent disagreement among annotators](https://md.research.elamur.ai/free/en/papers/pavlick-2019-inherent-disagreements-human-textual-inferences/) all resist simple solutions.
- **Representations and theory.** Debates continue over the right unit of meaning (senses, frames, graphs), the right syntactic representation, and whether one universal scheme can serve all languages fairly.
- **Evaluation.** Automatic metrics often reward surface overlap. Designing tests that track human judgement and expose [bias](https://md.research.elamur.ai/free/en/papers/rudinger-2018-gender-bias-coreference-resolution/) remains a central concern.
- **Pragmatics in machines.** Politeness, irony and indirect requests depend on social context; modelling them, as in work on [pragmatic reasoning in language games](https://md.research.elamur.ai/free/en/papers/frank-2012-predicting-pragmatic-reasoning-language-games/), remains an open frontier.

## Neighbouring topics

Linguistics shares most of its papers with [natural language processing](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/), which takes linguistic analysis as a component of downstream tasks like [question answering](https://md.research.elamur.ai/free/en/topics/question-answering/), [information extraction](https://md.research.elamur.ai/free/en/topics/information-extraction/) and [summarization](https://md.research.elamur.ai/free/en/topics/summarization/). The second-closest shelf is [machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/), together with its topic of the same name, [machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/): alignment, parallel corpora and translation metrics are linguistic problems as much as engineering ones. Smaller bridges lead to [Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/), to [history and humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/history/) through philology and ancient languages, to [law](https://md.research.elamur.ai/free/en/shelves/social-sciences/law/) through legal language and [legal AI](https://md.research.elamur.ai/free/en/topics/legal-ai/), and to [education](https://md.research.elamur.ai/free/en/shelves/social-sciences/education/) through [grammatical error correction and learner language](https://md.research.elamur.ai/free/en/topics/grammatical-error-correction/).

Neighbouring shelves: papers shared Artificial Intelligence shares the most: 8199 of the 9,206 papers here (89%) stand on both.

**The numbers**

| Shelf | Papers shared |
| --- | --- |
| [Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) | 8199 shared · 89% |
| [Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) | 1035 shared · 11% |
| [Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) | 434 shared · 5% |
| [Experimental and Cognitive Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) | 375 shared · 4% |
| [Literature and Literary Theory](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/literature-and-literary-theory/) | 312 shared · 3% |
| [Developmental and Educational Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/developmental-and-educational-psychology/) | 249 shared · 3% |
| [Signal Processing](https://md.research.elamur.ai/free/en/shelves/computer-science/signal-processing/) | 207 shared · 2% |
| [Philosophy](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/philosophy/) | 204 shared · 2% |
| [Religious studies](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/religious-studies/) | 180 shared · 2% |
| [Safety Research](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/) | 147 shared · 2% |

Within the shelf, the largest topics are the Semitic languages and coreference, followed by Hebrew and Aramaic, translation, [sentiment and emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) and discourse. Other topics connect linguistics to [multilingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/), [dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/), [speech](https://md.research.elamur.ai/free/en/topics/speech/), [reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/), [named entity recognition](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/) and [vision and language](https://md.research.elamur.ai/free/en/topics/multimodal/).

The topics on this shelf Semitic languages: 1338 of the 9,206 papers here.

**The numbers**

| Topic | Papers |
| --- | --- |
| [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) | 1,338 papers |
| [English](https://md.research.elamur.ai/free/en/topics/english/) | 1,318 papers |
| [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) | 1,230 papers |
| [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) | 934 papers |
| [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) | 639 papers |
| [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) | 550 papers |
| [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) | 467 papers |
| [Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) | 443 papers |
| [Hebrew & Aramaic](https://md.research.elamur.ai/free/en/topics/hebrew-aramaic/) | 440 papers |
| [Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) | 438 papers |
| [Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) | 393 papers |
| [Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) | 392 papers |

## Key works in this library

- [Speech Acts (Searle)](https://md.research.elamur.ai/free/en/papers/searle-1969-speech-acts/) — the classic theory of illocutionary acts.
- [Hedges (Lakoff)](https://md.research.elamur.ai/free/en/papers/lakoff-1973-hedges-study-meaning-criteria/) — fuzzy category membership and the words that adjust it.
- [Politeness: Some Universals in Language Usage](https://md.research.elamur.ai/free/en/papers/brown-1987-politeness-some-universals-language-usage/) — face and politeness strategies across cultures.
- [Rhetorical Structure Theory](https://md.research.elamur.ai/free/en/papers/mann-1988-rhetorical-structure-theory-functional/) — texts as trees of nucleus–satellite relations.
- [Word association norms (Church and Hanks)](https://md.research.elamur.ai/free/en/papers/church-1990-word-association-norms-mutual-information/) — corpus statistics for word associations.
- [The Penn Treebank (Marcus et al.)](https://md.research.elamur.ai/free/en/papers/marcus-1993-building-large-annotated-corpus-english/) — the founding English treebank.
- [Sentence alignment (Gale and Church)](https://md.research.elamur.ai/free/en/papers/gale-1993-program-aligning-sentences-bilingual-corpora/) — length-based alignment of parallel texts.
- [WordNet: A Lexical Database for English](https://md.research.elamur.ai/free/en/papers/miller-1994-wordnet/) — the standard English sense network.
- [Centering theory (Grosz, Joshi and Weinstein)](https://md.research.elamur.ai/free/en/papers/grosz-1995-centering-framework-modeling-local-coherence/) — attention and pronouns in local coherence.
- [The Berkeley FrameNet Project](https://md.research.elamur.ai/free/en/papers/baker-1998-berkeley-framenet/) — frame semantics as a lexical resource.
- [A Maximum-Entropy-Inspired Parser](https://md.research.elamur.ai/free/en/papers/charniak-2000-maximum-entropy-inspired-parser/) — a landmark statistical constituency parser.
- [The Cambridge Grammar of the English Language](https://md.research.elamur.ai/free/en/papers/huddleston-2002-cambridge-grammar-english-language/) — a modern reference grammar of English.
- [Multiword expressions (Sag et al.)](https://md.research.elamur.ai/free/en/papers/sag-2002-multiword-expressions-pain-neck-nlp/) — a classification of idioms and fixed phrases.
- [Automatic Labeling of Semantic Roles](https://md.research.elamur.ai/free/en/papers/gildea-2002-automatic-labeling-semantic-roles/) — the first statistical semantic role labeler.
- [BLEU](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/) — the most widely used translation metric.
- [Head-driven statistical parsing (Collins)](https://md.research.elamur.ai/free/en/papers/collins-2003-head-driven-statistical-models-natural/) — lexicalised generative parsing.
- [Accurate Unlexicalized Parsing](https://md.research.elamur.ai/free/en/papers/klein-2003-accurate-unlexicalized-parsing/) — structure over word statistics.
- [The Proposition Bank](https://md.research.elamur.ai/free/en/papers/palmer-2005-proposition-bank-annotated-corpus-semantic/) — verb-specific semantic roles over the treebank.
- [CoNLL-X shared task](https://md.research.elamur.ai/free/en/papers/buchholz-2006-conll-x-shared-task-multilingual/) — the first large multilingual parsing comparison.
- [The Penn Discourse TreeBank 2.0](https://md.research.elamur.ai/free/en/papers/prasad-2008-penn-discourse-treebank-2-0/) — connective-based discourse relations.
- [Information structure in discourse](https://md.research.elamur.ai/free/en/papers/roberts-2012-information-structure-discourse-towards-integrated/) — the Question Under Discussion model of pragmatics.
- [MADAMIRA](https://md.research.elamur.ai/free/en/papers/pasha-2014-madamira-fast-comprehensive-tool-morphological/) — morphological analysis and disambiguation for Arabic.
- [Farasa](https://md.research.elamur.ai/free/en/papers/abdelali-2016-farasa-fast-furious-segmenter-arabic/) — fast Arabic word segmentation.
- [Universal Dependencies v1](https://md.research.elamur.ai/free/en/papers/nivre-2016-universal-dependencies-v1-multilingual-treebank/) — one annotation scheme for many languages.
- [Deep biaffine attention (Dozat and Manning)](https://md.research.elamur.ai/free/en/papers/dozat-2016-deep-biaffine-attention-neural-dependency/) — a simple, strong neural graph-based parser.

## Papers (9,206)

**1** [2](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/page/2/) … [10](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/page/10/) … [93](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/page/93/) [next →](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/page/2/)

1. [**GloVe: Global Vectors for Word Representation**Jeffrey Pennington, Richard Socher, Christopher Manning*2014 · 38,574 citations*](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/)
2. [**Bleu: a Method for Automatic Evaluation of Machine Translation**Kishore Papineni, Salim Roukos, Todd Ward, et al.*2002 · 32,134 citations*](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/)
3. [**Latent Dirichlet Allocation**David M. Blei, Andrew Y. Ng, Michael I. Jordan*2003 · 28,336 citations*](https://md.research.elamur.ai/free/en/papers/blei-2003-latent-dirichlet-allocation/)
4. [**Neural Machine Translation by Jointly Learning to Align and Translate**Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio*2014 · 19,018 citations*](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/)
5. [**Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks**Nils Reimers, Iryna Gurevych*2019 · 17,026 citations*](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/)
6. [**Attention Is All You Need**Ashish Vaswani, Noam Shazeer, Niki Parmar, et al.*2017 · 16,593 citations*](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/)
7. [**Convolutional Neural Networks for Sentence Classification**Yoon Kim*2014 · 16,269 citations*](https://md.research.elamur.ai/free/en/papers/kim-2014-convolutional-neural-networks-sentence-classification/)
8. [**Transformers: State-of-the-Art Natural Language Processing**Thomas Wolf, Lysandre Debut, Victor Sanh, et al.*2020 · 15,606 citations*](https://md.research.elamur.ai/free/en/papers/wolf-2020-transformers-state-art-natural-language/)
9. [**Auto-Encoding Variational Bayes**Diederik P Kingma, Max Welling*2013 · 15,018 citations*](https://md.research.elamur.ai/free/en/papers/kingma-2013-auto-encoding-variational-bayes/)
10. [**ROUGE: A Package for Automatic Evaluation of Summaries**Chin-Yew Lin*2004 · 13,438 citations*](https://md.research.elamur.ai/free/en/papers/lin-2004-rouge-package-automatic-evaluation-summaries/)
11. [**BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension**Mike Lewis, Yinhan Liu, Naman Goyal, et al.*2020 · 12,784 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/)
12. [**GPT-4 Technical Report**OpenAI, Josh Achiam, Steven Adler, et al.*2023 · 12,732 citations*](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/)
13. [**Chain-of-Thought Prompting Elicits Reasoning in Large Language Models**Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.*2022 · 11,308 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/)
14. [**Politeness: Some Universals in Language Usage**Penelope Brown, Stephen C. Levinson, John J. Gumperz*1987 · 10,547 citations*](https://md.research.elamur.ai/free/en/papers/brown-1987-politeness-some-universals-language-usage/)
15. [**Effective Approaches to Attention-based Neural Machine Translation**Thang Luong, Hieu Pham, Christopher D. Manning*2015 · 9,655 citations*](https://md.research.elamur.ai/free/en/papers/luong-2015-effective-approaches-attention-based-neural/)
16. [**SQuAD: 100,000+ Questions for Machine Comprehension of Text**Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, et al.*2016 · 9,513 citations*](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/)
17. [**The Stanford CoreNLP Natural Language Processing Toolkit**Christopher Manning, Mihai Surdeanu, John Bauer, et al.*2014 · 8,629 citations*](https://md.research.elamur.ai/free/en/papers/manning-2014-stanford-corenlp-natural-language-processing/)
18. [**BioBERT: a pre-trained biomedical language representation model for biomedical text mining**Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, et al.*2019 · 8,311 citations*](https://md.research.elamur.ai/free/en/papers/lee-2019-biobert-pre-trained-biomedical-language/)
19. [**Text Categorization with Support Vector Machines: Learning with Many Relevant Features**Thorsten Joachims*1998 · 8,311 citations*](https://md.research.elamur.ai/free/en/papers/joachims-1998-text-categorization-support-vector-machines/)
20. [**Thumbs up? Sentiment Classification using Machine Learning Techniques**Bo Pang, Lillian Lee, Shivakumar Vaithyanathan*2002 · 7,762 citations*](https://md.research.elamur.ai/free/en/papers/pang-2002-thumbs-up-sentiment-classification-machine/)
21. [**On the Properties of Neural Machine Translation: Encoder-Decoder Approaches**Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, et al.*2014 · 7,756 citations*](https://md.research.elamur.ai/free/en/papers/cho-2014-properties-neural-machine-translation-encoder/)
22. [**GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding**Alex Wang, Amanpreet Singh, Julian Michael, et al.*2018 · 7,169 citations*](https://md.research.elamur.ai/free/en/papers/wang-2018-glue-multi-task-benchmark-analysis/)
23. [**Representation Learning with Contrastive Predictive Coding**Aaron van den Oord, Yazhe Li, Oriol Vinyals*2018 · 6,842 citations*](https://md.research.elamur.ai/free/en/papers/oord-2018-representation-learning-contrastive-predictive-coding/)
24. [**Speech Acts: An Essay in the Philosophy of Language**John R. Searle*1969 · 6,673 citations*](https://md.research.elamur.ai/free/en/papers/searle-1969-speech-acts/)
25. [**Natural Language Processing (almost) from Scratch**Ronan Collobert, Jason Weston, Leon Bottou, et al.*2011 · 6,138 citations*](https://md.research.elamur.ai/free/en/papers/collobert-2011-natural-language-processing-almost-scratch/)

**1** [2](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/page/2/) … [10](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/page/10/) … [93](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/page/93/) [next →](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/page/2/)

Updated 24 October 2026

- [← previous subfield History and Philosophy of Science](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/history-and-philosophy-of-science/)
- [next subfield → Literature and Literary Theory](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/literature-and-literary-theory/)
- [↑ back to Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/)