---
title: "Christopher D. Manning — papers and cards"
description: "Christopher D. Manning: 21 paper cards in the Research library (2003–2021), with what each paper is about, the ideas Mova took from it, and the co-authors."
canonical: "https://research.elamur.ai/free/en/authors/christopher-d-manning/"
---
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# Christopher D. Manning

21 paper cards in the Research library · 2003–2021

Christopher D. Manning wrote 21 of the papers Mova reads, in 2003–2021, most of them on [Universal Dependencies](https://md.research.elamur.ai/free/en/topics/universal-dependencies/), [Dependency parsing](https://md.research.elamur.ai/free/en/topics/dependency-parsing/), [Treebanks](https://md.research.elamur.ai/free/en/topics/treebanks/); often with [Marie-Catherine de Marneffe](https://md.research.elamur.ai/free/en/authors/marie-catherine-de-marneffe/), [Joakim Nivre](https://md.research.elamur.ai/free/en/authors/joakim-nivre/), [Sebastian Schuster](https://md.research.elamur.ai/free/en/authors/sebastian-schuster/).

[OpenAlex](https://openalex.org/A5046006076) · [ORCID](https://orcid.org/0000-0001-6155-649X) · [**h-index 139**](https://openalex.org/authors/A5046006076) · [180101 citations](https://openalex.org/authors/A5046006076) · [770 works](https://openalex.org/works?filter=author.id:A5046006076) — openalex-snapshot, 2026-10-14

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Christopher+D.+Manning) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Christopher+D.+Manning) · [dblp](https://dblp.org/search?q=Christopher+D.+Manning) · [ACL Anthology](https://aclanthology.org/search/?q=Christopher+D.+Manning) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Christopher+D.+Manning)

Topics: [Universal Dependencies](https://md.research.elamur.ai/free/en/topics/universal-dependencies/) 6 · [Dependency parsing](https://md.research.elamur.ai/free/en/topics/dependency-parsing/) 4 · [Treebanks](https://md.research.elamur.ai/free/en/topics/treebanks/) 4 · [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/) 3

## Papers

1. **[Universal Dependencies](https://md.research.elamur.ai/free/en/papers/demarneffe-2021-universal-dependencies/)** *(2021)* This article sets out the linguistic theory behind Universal Dependencies (UD), which is at once a cross-linguistically consistent morphosyntactic annotation scheme, an open research community and a growing collection of treebanks.
2. **[Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://md.research.elamur.ai/free/en/papers/ud-2020-qi-stanza-python-natural-language-processing/)** *(2020)* Stanza is a Python toolkit whose pipeline is neural from raw text to dependencies, for 66 languages. It has a combined tokenizer and sentence splitter, multi-word-token expansion (a frequency lexicon with a sequence-to-sequence model), a POS and feature tagger that conditions XPOS and features on UPOS, a lemmatizer…
3. **[Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://md.research.elamur.ai/free/en/papers/ud-2020-nivre-universal-dependencies-v2-evergrowing-multilingual/)** *(2020)* The paper describes the UD v2 guidelines and the state of the collection at release 2.5: 157 treebanks in 90 languages, built by more than 300 contributors.
4. **[Sentences with Gapping: Parsing and Reconstructing Elided Predicates](https://md.research.elamur.ai/free/en/papers/ud-2018-schuster-sentences-gapping-parsing-reconstructing-elided/)** *(2018)* In "Paul likes coffee and Mary tea" the verb is elided, so standard parsers attach the remnants badly and downstream relation extraction misses the predicate.
5. **[CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies](https://md.research.elamur.ai/free/en/papers/ud-2017-zeman-conll-2017-shared-task-multilingual/)** *(2017)* The shared task asks systems to parse raw text, with no gold tokenization, sentence splitting or tags, into UD trees for 49 languages.
6. **[Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task](https://md.research.elamur.ai/free/en/papers/ud-2017-dozat-stanford-s-graph-based-neural/)** *(2017)* The Stanford system is a biaffine graph parser: a BiLSTM encoder, separate ReLU layers for the head and dependent roles of arcs and labels, and biaffine scorers for arcs and labels.
7. **[Arc-swift: A Novel Transition System for Dependency Parsing](https://md.research.elamur.ai/free/en/papers/ud-2017-qi-arc-swift-novel-transition-system/)** *(2017)* Transition-based parsers reach a distant attachment only through a sequence of local shifts and reduces, and every step is a place to make an error.
8. **[Gapping Constructions in Universal Dependencies v2](https://md.research.elamur.ai/free/en/papers/ud-2017-schuster-gapping-constructions-universal-dependencies-v2/)** *(2017)* This is a design paper rather than an experiment. It asks how gapping (the omission of a repeated verb, as in "John likes tea, and Mary coffee") should be represented in UD v2. The authors survey the cross-linguistic patterns of gapping, including languages where the overt verb sits in the last conjunct, and compare…
9. **[Universal Dependencies v1: A Multilingual Treebank Collection](https://md.research.elamur.ai/free/en/papers/ud-2016-nivre-universal-dependencies-v1-multilingual-treebank/)** *(2016)* The paper is the reference for the first UD release. It sets out the design principles: dependency-based and lexicalist annotation, the recoverability principle (every syntactic word must map back to the raw text), content words as the backbone of the tree with function words as dependents, and a closed inventory of…
10. **[Deep Biaffine Attention for Neural Dependency Parsing](https://md.research.elamur.ai/free/en/papers/missing-2016-dozat-deep-biaffine-attention-neural-dependency/)** *(2016)* The paper builds on the neural graph-based parser of Kiperwasser and Goldberg. It uses a larger and more heavily regularised BiLSTM encoder, passes the top recurrent states through dimension-reducing MLPs, and replaces the MLP attention and the affine label classifier with biaffine ones: one for arc scores over all…
11. **[Enhanced English Universal Dependencies: An Improved Representation for Natural Language Understanding Tasks](https://md.research.elamur.ai/free/en/papers/ud-2016-schuster-enhanced-english-universal-dependencies-improved/)** *(2016)* Shallow understanding tasks such as relation extraction need direct links between content words, which a strict basic tree lacks: in "Fred started to laugh" the controlled verb has no subject edge.
12. **[Universal Stanford dependencies: A cross-linguistic typology](https://md.research.elamur.ai/free/en/papers/ud-2014-marneffe-universal-stanford-dependencies-cross-linguistic/)** *(2014)* The paper reconstructs the typology behind Stanford Dependencies and proposes a two-layer taxonomy: 42 universal grammatical relations that are broadly attested across languages, with language-specific subtypes added under them.
13. **[More Constructions, More Genres: Extending Stanford Dependencies](https://md.research.elamur.ai/free/en/papers/bank-2013-marneffe-more-constructions-more-genres-extending/)** *(2013)* The Stanford scheme was built on newswire and gives little analysis to constructions that are rare there but common in web text: tough adjectives, free relatives, comparatives, small clauses, questions, imperatives, fragments and lists.
14. **[Part-of-Speech Tagging from 97% to 100%: Is It Time for Some Linguistics?](https://md.research.elamur.ai/free/en/papers/enud-2011-manning-part-speech-tagging-97-100/)** *(2011)* Taggers report about 97% token accuracy, but only about 56% of sentences are fully right, and a single error can break a downstream parser.
15. **[Better Arabic Parsing: Baselines, Evaluations, and Analysis](https://md.research.elamur.ai/free/en/papers/aclbulk-2010-green-better-arabic-parsing-baselines-evaluations/)** *(2010)* Green and Manning ask why constituency parsers for Modern Standard Arabic lag behind English results on the Penn Arabic Treebank (ATB).
16. **[Was It Good? It Was Provocative.” Learning the Meaning of Scalar Adjectives](https://md.research.elamur.ai/free/en/papers/marneffe-2010-was-it-good-it-was/)** *(2010)* Indirect answers to yes/no questions often do not say "yes" or "no" outright. "Was it good? It was provocative" leaves the answer open, while "It was great" does not. The authors mine two kinds of evidence. First, scale order between gradable adjectives comes from online IMDB reviews: a word's expected star rating…
17. **[Multi-word expressions in textual inference: Much ado about nothing?](https://md.research.elamur.ai/free/en/papers/aclbulk-2009-marneffe-multi-word-expressions-textual-inference/)** *(2009)* The paper asks whether multi-word expressions (MWEs) matter for recognising textual entailment (RTE). It uses the manual Microsoft Research word alignments for the RTE2 data and counts the MWEs that appear as multi-word alignment links. Only 81 (dev) and 60 (test) true MWEs occur, in 8.2% of the 1,600 sentence pairs…
18. **[The Stanford Typed Dependencies Representation](https://md.research.elamur.ai/free/en/papers/marneffe-2008-stanford-typed-dependencies-representation/)** *(2008)* The paper describes Stanford typed dependencies (SD), a dependency scheme designed to make parser output usable by people outside linguistics who need to extract relations from text.
19. **[Generating Typed Dependency Parses from Phrase Structure Parses](https://md.research.elamur.ai/free/en/papers/map-2006-marneffe-generating-typed-dependency-parses-phrase/)** *(2006)* The paper describes a rule system that turns Penn Treebank phrase-structure trees, as produced by the Stanford parser, into typed dependencies.
20. **[LinGO Redwoods: A Rich and Dynamic Treebank for HPSG](https://md.research.elamur.ai/free/en/papers/enprag-2004-oepen-lingo-redwoods-rich-dynamic-treebank/)** *(2004)* Not read: this card is written from what is reliably known about the paper, and every idea is marked to check against the text.
21. **[Accurate Unlexicalized Parsing](https://md.research.elamur.ai/free/en/papers/aclbulk-2003-klein-accurate-unlexicalized-parsing/)** *(2003)* The paper asks how far a PCFG with no lexical items can go on the Penn Treebank. A vanilla treebank grammar parses badly because its independence assumptions are too strong. Adding simple linguistic state splits, such as parent annotation, marking of base noun phrases, tag splits for function words and horizontal…

## Co-authors (82)

- [Marie-Catherine de Marneffe](https://md.research.elamur.ai/free/en/authors/marie-catherine-de-marneffe/) ×10
- [Joakim Nivre](https://md.research.elamur.ai/free/en/authors/joakim-nivre/) ×6
- [Sebastian Schuster](https://md.research.elamur.ai/free/en/authors/sebastian-schuster/) ×5
- [Daniel Zeman](https://md.research.elamur.ai/free/en/authors/daniel-zeman/) ×4
- [Filip Ginter](https://md.research.elamur.ai/free/en/authors/filip-ginter/) ×4
- [Timothy Dozat](https://md.research.elamur.ai/free/en/authors/timothy-dozat/) ×4
- [Jan Hajič](https://md.research.elamur.ai/free/en/authors/jan-hajic/) ×3
- [Natalia Silveira](https://md.research.elamur.ai/free/en/authors/natalia-silveira/) ×3
- [Peng Qi](https://md.research.elamur.ai/free/en/authors/peng-qi/) ×3
- [Sampo Pyysalo](https://md.research.elamur.ai/free/en/authors/sampo-pyysalo/) ×3
- [Francis Tyers](https://md.research.elamur.ai/free/en/authors/francis-tyers/) ×2
- [Slav Petrov](https://md.research.elamur.ai/free/en/authors/slav-petrov/) ×2
- Ali Elkahky
- [Aljoscha Burchardt](https://md.research.elamur.ai/free/en/authors/aljoscha-burchardt/)
- Anna Missilä
- [Anna Nedoluzhko](https://md.research.elamur.ai/free/en/authors/anna-nedoluzhko/)
- Antonio Stella
- Atsuko Shimada
- Bill MacCartney
- Çağrı Çöltekin
- [Christopher Potts](https://md.research.elamur.ai/free/en/authors/christopher-potts/)
- [Dan Flickinger](https://md.research.elamur.ai/free/en/authors/dan-flickinger/)
- [Dan Klein](https://md.research.elamur.ai/free/en/authors/dan-klein/)
- [Dima Taji](https://md.research.elamur.ai/free/en/authors/dima-taji/)
- Elena Badmaeva
- [Emily Pitler](https://md.research.elamur.ai/free/en/authors/emily-pitler/)
- Esha Banerjee
- Georg Rehm
- Gustavo Mendonça
- [Hans Uszkoreit](https://md.research.elamur.ai/free/en/authors/hans-uszkoreit/)
- Hector Fernandez Alcalde
- Héctor Martínez Alonso
- Herman Leung
- Hiroshi Kanayama
- Jan Hajič jr.
- Jana Strnadová
- Jaroslava Hlaváčová
- Jason Bolton
- [Jenna Kanerva](https://md.research.elamur.ai/free/en/authors/jenna-kanerva/)
- Jesse Kirchner
- Josie Li
- Juhani Luotolahti
- [Katri Haverinen](https://md.research.elamur.ai/free/en/authors/katri-haverinen/)
- Katrin Marheinecke
- Kim Harris
- [Kira Droganova](https://md.research.elamur.ai/free/en/authors/kira-droganova/)
- [Kristina Toutanova](https://md.research.elamur.ai/free/en/authors/kristina-toutanova/)
- Manuela Sanguinetti
- [Maria Simi](https://md.research.elamur.ai/free/en/authors/maria-simi/)
- [Martin Popel](https://md.research.elamur.ai/free/en/authors/martin-popel/)
- [Martin Potthast](https://md.research.elamur.ai/free/en/authors/martin-potthast/)
- [Matthew Lamm](https://md.research.elamur.ai/free/en/authors/matthew-lamm/)
- Memduh Gokirmak
- Michael Mandl
- [Milan Straka](https://md.research.elamur.ai/free/en/authors/milan-straka/)
- Miriam Connor
- [Mohammed Attia](https://md.research.elamur.ai/free/en/authors/mohammed-attia/)
- [Nizar Habash](https://md.research.elamur.ai/free/en/authors/nizar-habash/)
- Rattima Nitisaroj
- [Reut Tsarfaty](https://md.research.elamur.ai/free/en/authors/reut-tsarfaty/)
- Ruli Manurung
- [Ryan McDonald](https://md.research.elamur.ai/free/en/authors/ryan-mcdonald/)
- [Samuel R. Bowman](https://md.research.elamur.ai/free/en/authors/samuel-r-bowman/)
- Saran Lertpradit
- [Sebastian Padó](https://md.research.elamur.ai/free/en/authors/sebastian-pado/)
- Silvie Cinková
- Siva Reddy
- Sookyoung Kwak
- Spence Green
- [Stephan Oepen](https://md.research.elamur.ai/free/en/authors/stephan-oepen/)
- Stina Ojala
- Tatiana Lando
- Tolga Kayadelen
- Umut Sulubacak
- Václava Kettnerová
- Valeria de Paiva
- [Vivien Macketanz](https://md.research.elamur.ai/free/en/authors/vivien-macketanz/)
- [Yoav Goldberg](https://md.research.elamur.ai/free/en/authors/yoav-goldberg/)
- Yuhao Zhang
- Yuhui Zhang
- [Zdeňka Urešová](https://md.research.elamur.ai/free/en/authors/zdenka-uresova/)
- Zhuoran Yu

## More papers in the library (25)

Pages of the Research library: our summary, the ideas, references and who cites each.

- [Mechanisms vs. Outcomes: Probing for Syntax Fails to Explain Performance on Targeted Syntactic Evaluations](https://md.research.elamur.ai/free/en/papers/agarwal-2025-mechanisms-vs-outcomes-probing-syntax/) — Ananth Agarwal, Jasper Jian, Christopher D Manning, et al. · 2025
- [Universal Dependencies for Sindhi](https://md.research.elamur.ai/free/en/papers/bauer-2025-universal-dependencies-sindhi/) — John Bauer, Sakiina Shah, Muhammad Shaheer, et al. · 2025
- [Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools](https://md.research.elamur.ai/free/en/papers/magesh-2024-hallucination-free-assessing-reliability-leading/) — Varun Magesh, Faiz Surani, Matthew Dahl, et al. · 2024
- [MSCAW-coref: Multilingual, Singleton and Conjunction-Aware Word-Level Coreference Resolution](https://md.research.elamur.ai/free/en/papers/liu-2024-mscaw-coref-multilingual-singleton-conjunction/) — Houjun Liu, John Bauer, Karel D'Oosterlinck, et al. · 2024
- [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://md.research.elamur.ai/free/en/papers/sarthi-2024-raptor-recursive-abstractive-processing-tree/) — Parth Sarthi, Salman Abdullah, Aditi Tuli, et al. · 2024
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://md.research.elamur.ai/free/en/papers/mitchell-2023-detectgpt-zero-shot-machine-generated/) — Eric Mitchell, Yoonho Lee, Alexander Khazatsky, et al. · 2023
- [Fine-tuning Language Models for Factuality](https://md.research.elamur.ai/free/en/papers/tian-2023-fine-tuning-language-models-factuality/) — Katherine Tian, Eric Mitchell, Huaxiu Yao, et al. · 2023
- [A Structural Probe for Finding Syntax in Word Representations](https://md.research.elamur.ai/free/en/papers/hewitt-2019-structural-probe-finding-syntax-word/) — John Hewitt, Christopher D. Manning · 2019
- [CoQA: A Conversational Question Answering Challenge](https://md.research.elamur.ai/free/en/papers/reddy-2019-coqa-conversational-question-answering-challenge/) — Siva Reddy, Danqi Chen, Christopher D. Manning · 2019
- [GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering](https://md.research.elamur.ai/free/en/papers/hudson-2019-gqa-new-dataset-real-world/) — Drew A. Hudson, Christopher D. Manning · 2019
- [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://md.research.elamur.ai/free/en/papers/zhang-2018-graph-convolution-over-pruned-dependency/) — Yuhao Zhang, Peng Qi, Christopher D. Manning · 2018
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/) — Zhilin Yang, Peng Qi, Saizheng Zhang, et al. · 2018
- [Semi-Supervised Sequence Modeling with Cross-View Training](https://md.research.elamur.ai/free/en/papers/clark-2018-semi-supervised-sequence-modeling-cross/) — Kevin Clark, Minh-Thang Luong, Christopher D. Manning, et al. · 2018
- [A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task](https://md.research.elamur.ai/free/en/papers/chen-2016-thorough-examination-cnn-daily-mail/) — Danqi Chen, Jason Bolton, Christopher D. Manning · 2016
- [Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models](https://md.research.elamur.ai/free/en/papers/luong-2016-achieving-open-vocabulary-neural-machine/) — Minh-Thang Luong, Christopher D. Manning · 2016
- [Deep Reinforcement Learning for Mention-Ranking Coreference Models](https://md.research.elamur.ai/free/en/papers/clark-2016-deep-reinforcement-learning-mention-ranking/) — Kevin Clark, Christopher D. Manning · 2016
- [Improving Coreference Resolution by Learning Entity-Level Distributed Representations](https://md.research.elamur.ai/free/en/papers/clark-2016-improving-coreference-resolution-learning-entity/) — Kevin Clark, Christopher D. Manning · 2016
- [Bilingual Word Representations with Monolingual Quality in Mind](https://md.research.elamur.ai/free/en/papers/luong-2015-bilingual-word-representations-monolingual-quality/) — Thang Luong, Hieu Pham, Christopher D. Manning · 2015
- [Entity-Centric Coreference Resolution with Model Stacking](https://md.research.elamur.ai/free/en/papers/clark-2015-entity-centric-coreference-resolution-model/) — Kevin Clark, Christopher D. Manning · 2015
- [Leveraging Linguistic Structure For Open Domain Information Extraction](https://md.research.elamur.ai/free/en/papers/angeli-2015-leveraging-linguistic-structure-open-domain/) — Gabor Angeli, Melvin Jose Johnson Premkumar, Christopher D. Manning · 2015
- [Grounded Compositional Semantics for Finding and Describing Images with Sentences](https://md.research.elamur.ai/free/en/papers/socher-2014-grounded-compositional-semantics-finding-describing/) — Richard Socher, Andrej Karpathy, Quoc V. Le, et al. · 2014
- [Did It Happen? The Pragmatic Complexity of Veridicality Assessment](https://md.research.elamur.ai/free/en/papers/marneffe-2012-did-it-happen-pragmatic-complexity/) — Marie-Catherine de Marneffe, Christopher D. Manning, Christopher Potts · 2012
- [Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions](https://md.research.elamur.ai/free/en/papers/socher-2011-semi-supervised-recursive-autoencoders-predicting/) — Richard Socher, Jeffrey Pennington, Eric H. Huang, et al. · 2011
- [Enforcing Transitivity in Coreference Resolution](https://md.research.elamur.ai/free/en/papers/finkel-2008-enforcing-transitivity-coreference-resolution/) — Jenny Rose Finkel, Christopher D. Manning · 2008
- [Optimizing Chinese Word Segmentation for Machine Translation Performance](https://md.research.elamur.ai/free/en/papers/chang-2008-optimizing-chinese-word-segmentation-machine/) — Pi-Chuan Chang, Michel Galley, Christopher D. Manning · 2008