---
title: "Language and Linguistics — papers"
description: "Language and Linguistics: 7,357 papers of the Elamur Research library · page 1 of 75 · 1–25, the most cited first — our summary of each, its ideas…"
canonical: "https://research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/"
---
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# 🗣️ Language and Linguistics

7,357 papers · page 1 of 75 · 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 1987–2023; the dot grows with the citations.

**The numbers**

| Paper | Citations |
| --- | --- |
| [1987 — Politeness: Some Universals in Language Usage](https://md.research.elamur.ai/free/en/papers/brown-1987-politeness-some-universals-language-usage/) | 10,545 |
| [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/) | 32,065 |
| [2003 — Latent Dirichlet Allocation](https://md.research.elamur.ai/free/en/papers/blei-2003-latent-dirichlet-allocation/) | 28,297 |
| [2013 — Auto-Encoding Variational Bayes](https://md.research.elamur.ai/free/en/papers/kingma-2013-auto-encoding-variational-bayes/) | 14,991 |
| [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/) | 18,908 |
| [2014 — Convolutional Neural Networks for Sentence Classification](https://md.research.elamur.ai/free/en/papers/kim-2014-convolutional-neural-networks-sentence-classification/) | 16,218 |
| [2016 — SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/) | 9,488 |
| [2017 — Attention Is All You Need](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/) | 16,413 |
| [2020 — Transformers: State-of-the-Art Natural Language Processing](https://md.research.elamur.ai/free/en/papers/wolf-2020-transformers-state-art-natural-language/) | 15,591 |
| [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/) | 12,752 |
| [2022 — Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/) | 11,278 |
| [2023 — GPT-4 Technical Report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/) | 12,694 |

### 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 7,353 dated papers from 1818 to 2026 (36 before 1966 are not drawn); the busiest year is 2026 with 922, and 65% of them appeared in 2018 or later.

**The numbers**

| Year | Papers |
| --- | --- |
| 2026 | 922 |
| 2025 | 360 |
| 2024 | 505 |
| 2023 | 701 |
| 2022 | 416 |
| 2021 | 461 |
| 2020 | 563 |
| 2019 | 448 |
| 2018 | 381 |
| 2017 | 293 |
| 2016 | 316 |
| 2015 | 180 |
| 2014 | 198 |
| 2013 | 125 |
| 2012 | 155 |
| 2011 | 116 |
| 2010 | 136 |
| 2009 | 97 |
| 2008 | 102 |
| 2007 | 84 |
| 2006 | 100 |
| 2005 | 67 |
| 2004 | 104 |
| 2003 | 59 |
| 2002 | 57 |
| 2001 | 24 |
| 2000 | 40 |
| 1999 | 35 |
| 1998 | 36 |
| 1997 | 31 |
| 1996 | 22 |
| 1995 | 25 |
| 1994 | 34 |
| 1993 | 14 |
| 1992 | 16 |
| 1991 | 16 |
| 1990 | 15 |
| 1989 | 6 |
| 1988 | 13 |
| 1987 | 9 |
| 1986 | 7 |
| 1985 | 2 |
| 1983 | 1 |
| 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 2,036,256 citations of the papers on this shelf.

**The numbers**

| Published | Papers and their citations |
| --- | --- |
| 2025–2029 | 1,282 papers, 20,601 citations |
| 2020–2024 | 2,646 papers, 625,705 citations |
| 2015–2019 | 1,618 papers, 644,582 citations |
| 2010–2014 | 730 papers, 259,077 citations |
| 2005–2009 | 450 papers, 153,048 citations |
| 2000–2004 | 284 papers, 195,189 citations |
| 1995–1999 | 149 papers, 70,119 citations |
| 1990–1994 | 95 papers, 36,888 citations |
| 1985–1989 | 37 papers, 21,824 citations |
| 1980–1984 | 8 papers, 271 citations |
| 1975–1979 | 13 papers, 753 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 80 papers; the 12 authors shown wrote 606 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/) | 80 papers · 7,339 citations |
| [Christopher Manning](https://md.research.elamur.ai/free/en/authors/christopher-d-manning/) | 76 papers · 72,626 citations |
| [Luke Zettlemoyer](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/) | 63 papers · 41,049 citations |
| [Noah A. Smith](https://md.research.elamur.ai/free/en/authors/noah-a-smith/) | 55 papers · 16,597 citations |
| [Massimo Poesio](https://md.research.elamur.ai/free/en/authors/massimo-poesio/) | 46 papers · 5,005 citations |
| [Iryna Gurevych](https://md.research.elamur.ai/free/en/authors/iryna-gurevych/) | 45 papers · 17,662 citations |
| [Yoav Goldberg](https://md.research.elamur.ai/free/en/authors/yoav-goldberg/) | 44 papers · 13,619 citations |
| [Graham Neubig](https://md.research.elamur.ai/free/en/authors/graham-neubig/) | 42 papers · 18,239 citations |
| [Philipp Koehn](https://md.research.elamur.ai/free/en/authors/philipp-koehn/) | 41 papers · 18,506 citations |
| [Yejin Choi](https://md.research.elamur.ai/free/en/authors/yejin-choi/) | 39 papers · 14,649 citations |
| [Nathan Schneider](https://md.research.elamur.ai/free/en/authors/nathan-schneider/) | 39 papers · 4,805 citations |
| [Vincent Ng](https://md.research.elamur.ai/free/en/authors/vincent-ng/) | 36 papers · 4,372 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 403 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/) | 403 papers |
| [Google](https://md.research.elamur.ai/free/en/companies/google/) | 385 papers |
| [Stanford University](https://md.research.elamur.ai/free/en/universities/stanford-university/) | 374 papers |
| [University of Washington](https://md.research.elamur.ai/free/en/universities/university-of-washington/) | 303 papers |
| [Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/) | 279 papers |
| [New York University](https://md.research.elamur.ai/free/en/universities/new-york-university/) | 226 papers |
| [Tsinghua University](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) | 219 papers |
| [University of Edinburgh](https://md.research.elamur.ai/free/en/universities/university-of-edinburgh/) | 203 papers |
| [Google DeepMind](https://md.research.elamur.ai/free/en/companies/google-deepmind/) | 199 papers |
| [Mohamed bin Zayed University of Artificial Intelligence](https://md.research.elamur.ai/free/en/universities/mohamed-bin-zayed-university-of-artificial-intelligence/) | 192 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 3,191 papers on this shelf.

**The numbers**

| Country | Papers |
| --- | --- |
| [United States](https://md.research.elamur.ai/free/en/countries/united-states/) | 3,191 papers |
| [China](https://md.research.elamur.ai/free/en/countries/china/) | 1,294 papers |
| [United Kingdom](https://md.research.elamur.ai/free/en/countries/united-kingdom/) | 974 papers |
| [Germany](https://md.research.elamur.ai/free/en/countries/germany/) | 652 papers |
| [Canada](https://md.research.elamur.ai/free/en/countries/canada/) | 328 papers |
| [Israel](https://md.research.elamur.ai/free/en/countries/israel/) | 321 papers |
| [France](https://md.research.elamur.ai/free/en/countries/france/) | 286 papers |
| Hong Kong | 282 papers |
| [United Arab Emirates](https://md.research.elamur.ai/free/en/countries/united-arab-emirates/) | 272 papers |
| [Netherlands](https://md.research.elamur.ai/free/en/countries/netherlands/) | 257 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, 967 of 7357.

**The numbers**

| Venue | Papers |
| --- | --- |
| [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) | 967 papers |
| [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/) | 730 papers |
| [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) | 663 papers |
| [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/) | 308 papers |
| [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/) | 227 papers |
| [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/) | 217 papers |
| [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/) | 170 papers |
| [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/) | 127 papers |
| [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/) | 127 papers |
| [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/) | 122 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: 6596 of the 7,357 papers here (90%) stand on both.

**The numbers**

| Shelf | Papers shared |
| --- | --- |
| [Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) | 6596 shared · 90% |
| [Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) | 810 shared · 11% |
| [Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) | 325 shared · 4% |
| [Experimental and Cognitive Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) | 270 shared · 4% |
| [Literature and Literary Theory](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/literature-and-literary-theory/) | 253 shared · 3% |
| [Developmental and Educational Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/developmental-and-educational-psychology/) | 194 shared · 3% |
| [Religious studies](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/religious-studies/) | 178 shared · 2% |
| [Philosophy](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/philosophy/) | 147 shared · 2% |
| [Signal Processing](https://md.research.elamur.ai/free/en/shelves/computer-science/signal-processing/) | 147 shared · 2% |
| [Safety Research](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/) | 111 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: 1201 of the 7,357 papers here.

**The numbers**

| Topic | Papers |
| --- | --- |
| [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) | 1,201 papers |
| [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) | 982 papers |
| [English](https://md.research.elamur.ai/free/en/topics/english/) | 957 papers |
| [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) | 854 papers |
| [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) | 558 papers |
| [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) | 537 papers |
| [Hebrew & Aramaic](https://md.research.elamur.ai/free/en/topics/hebrew-aramaic/) | 432 papers |
| [Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) | 368 papers |
| [Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) | 355 papers |
| [Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) | 336 papers |
| [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) | 326 papers |
| [Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) | 247 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

**1** [2](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/page/2/) … [75](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/page/75/) [next →](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/page/2/)

1. [**Bleu: a Method for Automatic Evaluation of Machine Translation**Kishore Papineni, Salim Roukos, Todd Ward, et al.*2002 · 32,065 citations*](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/)
2. [**Latent Dirichlet Allocation**David M. Blei, Andrew Y. Ng, Michael I. Jordan*2003 · 28,297 citations*](https://md.research.elamur.ai/free/en/papers/blei-2003-latent-dirichlet-allocation/)
3. [**Neural Machine Translation by Jointly Learning to Align and Translate**Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio*2014 · 18,908 citations*](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/)
4. [**Attention Is All You Need**Ashish Vaswani, Noam Shazeer, Niki Parmar, et al.*2017 · 16,413 citations*](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/)
5. [**Convolutional Neural Networks for Sentence Classification**Yoon Kim*2014 · 16,218 citations*](https://md.research.elamur.ai/free/en/papers/kim-2014-convolutional-neural-networks-sentence-classification/)
6. [**Transformers: State-of-the-Art Natural Language Processing**Thomas Wolf, Lysandre Debut, Victor Sanh, et al.*2020 · 15,591 citations*](https://md.research.elamur.ai/free/en/papers/wolf-2020-transformers-state-art-natural-language/)
7. [**Auto-Encoding Variational Bayes**Diederik P Kingma, Max Welling*2013 · 14,991 citations*](https://md.research.elamur.ai/free/en/papers/kingma-2013-auto-encoding-variational-bayes/)
8. [**BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension**Mike Lewis, Yinhan Liu, Naman Goyal, et al.*2020 · 12,752 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/)
9. [**GPT-4 Technical Report**OpenAI, Josh Achiam, Steven Adler, et al.*2023 · 12,694 citations*](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/)
10. [**Chain-of-Thought Prompting Elicits Reasoning in Large Language Models**Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.*2022 · 11,278 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/)
11. [**Politeness: Some Universals in Language Usage**Penelope Brown, Stephen C. Levinson, John J. Gumperz*1987 · 10,545 citations*](https://md.research.elamur.ai/free/en/papers/brown-1987-politeness-some-universals-language-usage/)
12. [**SQuAD: 100,000+ Questions for Machine Comprehension of Text**Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, et al.*2016 · 9,488 citations*](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/)
13. [**The Stanford CoreNLP Natural Language Processing Toolkit**Christopher Manning, Mihai Surdeanu, John Bauer, et al.*2014 · 8,620 citations*](https://md.research.elamur.ai/free/en/papers/manning-2014-stanford-corenlp-natural-language-processing/)
14. [**Text Categorization with Support Vector Machines: Learning with Many Relevant Features**Thorsten Joachims*1998 · 8,304 citations*](https://md.research.elamur.ai/free/en/papers/joachims-1998-text-categorization-support-vector-machines/)
15. [**BioBERT: a pre-trained biomedical language representation model for biomedical text mining**Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, et al.*2019 · 8,281 citations*](https://md.research.elamur.ai/free/en/papers/lee-2019-biobert-pre-trained-biomedical-language/)
16. [**Thumbs up? Sentiment Classification using Machine Learning Techniques**Bo Pang, Lillian Lee, Shivakumar Vaithyanathan*2002 · 7,756 citations*](https://md.research.elamur.ai/free/en/papers/pang-2002-thumbs-up-sentiment-classification-machine/)
17. [**On the Properties of Neural Machine Translation: Encoder-Decoder Approaches**Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, et al.*2014 · 7,734 citations*](https://md.research.elamur.ai/free/en/papers/cho-2014-properties-neural-machine-translation-encoder/)
18. [**GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding**Alex Wang, Amanpreet Singh, Julian Michael, et al.*2018 · 7,141 citations*](https://md.research.elamur.ai/free/en/papers/wang-2018-glue-multi-task-benchmark-analysis/)
19. [**Representation Learning with Contrastive Predictive Coding**Aaron van den Oord, Yazhe Li, Oriol Vinyals*2018 · 6,826 citations*](https://md.research.elamur.ai/free/en/papers/oord-2018-representation-learning-contrastive-predictive-coding/)
20. [**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/)
21. [**Natural Language Processing (almost) from Scratch**Ronan Collobert, Jason Weston, Leon Bottou, et al.*2011 · 6,099 citations*](https://md.research.elamur.ai/free/en/papers/collobert-2011-natural-language-processing-almost-scratch/)
22. [**GloVe: Global Vectors for Word Representation**Jeffrey Pennington, Richard Socher, Christopher Manning*2014 · 5,962 citations*](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/)
23. [**Survey of Hallucination in Natural Language Generation**Ziwei Ji, Nayeon Lee, Rita Frieske, et al.*2022 · 5,947 citations*](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/)
24. [**Emerging Properties in Self-Supervised Vision Transformers**Mathilde Caron, Hugo Touvron, Ishan Misra, et al.*2021 · 5,910 citations*](https://md.research.elamur.ai/free/en/papers/caron-2021-emerging-properties-self-supervised-vision/)
25. [**Distributed Representations of Sentences and Documents**Quoc V. Le, Tomas Mikolov*2014 · 5,747 citations*](https://md.research.elamur.ai/free/en/papers/le-2014-distributed-representations-sentences-documents/)

**1** [2](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/page/2/) … [75](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/page/75/) [next →](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/page/2/)

Updated 23 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/)