---
title: "Christopher E. Potts — author profile and papers · Reasoning"
description: "Christopher E. Potts, Stanford University: 33 papers on Elamur Research (2009–2025), h-index 51 — affiliations, topics, co-authors and who…"
canonical: "https://research.elamur.ai/free/en/authors/christopher-e-potts/"
---
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# Christopher E. Potts

Christopher E. Potts has 33 papers in our library, in 2009–2025, most of them on [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/), [English](https://md.research.elamur.ai/free/en/topics/english/), [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/); often with [Christopher D. Manning](https://md.research.elamur.ai/free/en/authors/christopher-d-manning/), [Noah D. Goodman](https://md.research.elamur.ai/free/en/authors/noah-d-goodman/), [Atticus Geiger](https://md.research.elamur.ai/free/en/authors/atticus-geiger/). 3 of them are among the papers read by [Mova, the project](https://md.research.elamur.ai/free/en/projects/mova/).

33 papers in our library · 2009–2025 · page 1 of 2

[OpenAlex](https://openalex.org/A5042601761) · [ORCID](https://orcid.org/0000-0002-7978-6055) · [**h-index 51**](https://openalex.org/authors/A5042601761) · [266 works](https://openalex.org/works?filter=author.id:A5042601761) · [26,650 citations](https://openalex.org/works?filter=author.id:A5042601761&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 33 works in our library](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/#papers) · [🔗 Cited 11,943 times in our library](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/) · [237 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #118](https://md.research.elamur.ai/free/en/paper-stars/page/2/#star-118)

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+E.+Potts) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Christopher+E.+Potts) · [dblp](https://dblp.org/search?q=Christopher+E.+Potts) · [ACL Anthology](https://aclanthology.org/search/?q=Christopher+E.+Potts)

## Affiliations

[🎓**Stanford University** Stanford · United States 2007–2026 · 212 papersEmployment · Professor, Linguistics*current*](https://md.research.elamur.ai/free/en/universities/stanford-university/) · [🎓**University of Massachusetts Amherst** Amherst Center · United States 2003–2015 · 12 papers](https://md.research.elamur.ai/free/en/universities/university-of-massachusetts-amherst/) · [🎓**University of California, Santa Cruz** Santa Cruz · United States 2002 · 3 papers](https://md.research.elamur.ai/free/en/universities/university-of-california-santa-cruz/) · [🎓**University of California San Diego** San Diego · United States 2015–2024 · 2 papers](https://md.research.elamur.ai/free/en/universities/university-of-california-san-diego/) · [🏢**Google** Mountain View · United States 2015–2022 · 2 papers](https://md.research.elamur.ai/free/en/companies/google/)

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 more institutions on single bylines are not listed.

## Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 14 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 32 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 31 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Language and Linguistics · 31 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Artificial Intelligence · 31 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Information Systems · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · [Computer Vision and Pattern Recognition · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Psychology · 4 works](https://md.research.elamur.ai/free/en/shelves/psychology/) · [Developmental and Educational Psychology · 3 works](https://md.research.elamur.ai/free/en/shelves/psychology/developmental-and-educational-psychology/) · [Experimental and Cognitive Psychology · 2 works](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · [History and Philosophy of Science · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/history-and-philosophy-of-science/) · [Mathematics · 1 work](https://md.research.elamur.ai/free/en/shelves/mathematics/) · [Statistics and Probability · 1 work](https://md.research.elamur.ai/free/en/shelves/mathematics/statistics-and-probability/) · [Cognitive Neuroscience · 1 work](https://md.research.elamur.ai/free/en/shelves/neuroscience/cognitive-neuroscience/) · [Neuroscience · 1 work](https://md.research.elamur.ai/free/en/shelves/neuroscience/)

🧭 Topics 13 Research subjects and languages the work is about.[Sentiment & emotion · 3 works](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · [Reasoning · 5 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Treebanks · 1 work](https://md.research.elamur.ai/free/en/topics/treebanks/) · [Information retrieval & search · 3 works](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · [Language models & pretraining · 3 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [English · 4 works](https://md.research.elamur.ai/free/en/topics/english/) · [Interpretability · 2 works](https://md.research.elamur.ai/free/en/topics/interpretability/) · [Vision & language · 2 works](https://md.research.elamur.ai/free/en/topics/multimodal/) · [Implicature · 2 works](https://md.research.elamur.ai/free/en/topics/implicature/) · [Coreference resolution · 3 works](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) · [Discourse analysis · 1 work](https://md.research.elamur.ai/free/en/topics/discourse-analysis/) · [Lexical semantics & word embeddings · 1 work](https://md.research.elamur.ai/free/en/topics/lexical-semantics/) · [Multilingual & cross-lingual NLP · 1 work](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/)

💡 Concepts 8 Key ideas and methods explained on our concept pages.[Natural language processing · 1 work](https://md.research.elamur.ai/free/en/concepts/natural-language-processing/) · [Semantics · 1 work](https://md.research.elamur.ai/free/en/concepts/semantics/) · [Linguistic annotation · 1 work](https://md.research.elamur.ai/free/en/concepts/annotation/) · [Natural language understanding · 1 work](https://md.research.elamur.ai/free/en/concepts/natural-language-understanding/) · [GloVe · 1 work](https://md.research.elamur.ai/free/en/concepts/glove/) · [Word embeddings · 1 work](https://md.research.elamur.ai/free/en/concepts/word-embeddings/) · [Retrieval-augmented generation · 1 work](https://md.research.elamur.ai/free/en/concepts/retrieval-augmented-generation/) · [Implicature · 2 works](https://md.research.elamur.ai/free/en/concepts/implicature/)

## Works by year

### 2020s

[**2025** 2 papers](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/year/2025/) · [**2024** 4 papers](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/year/2024/) · [**2023** 4 papers](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/year/2023/) · [**2022** 3 papers](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/year/2022/) · [**2021** 1 paper](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/year/2021/) · [**2020** 2 papers](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/year/2020/)

### 2010s

[**2019** 2 papers](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/year/2019/) · [**2018** 3 papers](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/year/2018/) · [**2017** 1 paper](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/year/2017/) · [**2015** 2 papers](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/year/2015/) · [**2013** 5 papers](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/year/2013/) · [**2012** 1 paper](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/year/2012/) · [**2011** 1 paper](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/year/2011/) · [**2010** 1 paper](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/year/2010/)

### 2000s

[**2009** 1 paper](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/year/2009/)

## Papers

The most cited first (OpenAlex and our library).

**1** [2](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/page/2/) [next →](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/page/2/)

1. **[Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank](https://md.research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/)** *(2013 · 9,138 citations)* Vectors of individual words poorly capture the meaning of longer phrases. Studying how the sentiment of a sentence is composed from its parts was hampered by a lack of large annotated resources. The authors created the Stanford Sentiment Treebank. They took a corpus of short movie review excerpts from…
2. **[A large annotated corpus for learning natural language inference](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/)** *(2015 · 5,299 citations)* The paper addresses the task of natural language inference (NLI). The goal is to determine whether the second sentence follows from the first, contradicts it, or is unrelated to it. The authors note that existing corpora for this task suffer from three shortcomings. They are too small for modern models, some of them…
3. **[Learning Word Vectors for Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/maas-2011-learning-word-vectors-sentiment-analysis/)** *(2011 · 4,333 citations)* Word vectors learned without supervision capture topical similarity well but mostly ignore sentiment. Words such as wonderful and terrible can end up close together because they appear in similar contexts. Maas et al. propose a model that learns word vectors from two signals at once. The first is an unsupervised…
4. **[ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://md.research.elamur.ai/free/en/papers/santhanam-2022-colbertv2-effective-efficient-retrieval-lightweight/)** *(2022 · 485 citations)* Late interaction retrievers such as ColBERT give every query and passage token its own vector. They score relevance by letting each query token find its best-matching passage token and then adding up those matches. This is more expressive than compressing a whole text into one vector. The cost is size: storing a…
5. **[Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP](https://md.research.elamur.ai/free/en/papers/khattab-2022-demonstrate-search-predict-composing-retrieval/)** *(2022 · 222 citations)* Pairing a frozen language model with a retriever usually follows a fixed recipe: fetch passages for the query, insert them into the prompt, and let the model answer.
6. **[Did It Happen? The Pragmatic Complexity of Veridicality Assessment](https://md.research.elamur.ai/free/en/papers/marneffe-2012-did-it-happen-pragmatic-complexity/)** *(2012 · 202 citations)* Event and relation extraction systems usually treat any relation they extract as true. They largely ignore veridicality, meaning whether a reader takes a described event to have actually happened. Earlier work mostly treated veridicality as a fixed lexical property, such as believe being non-veridical or realize…
7. **[The Life and Death of Discourse Entities: Identifying Singleton Mentions](https://md.research.elamur.ai/free/en/papers/recasens-2013-life-death-discourse-entities-identifying/)** *(2013 · 166 citations)* Most entities introduced in a text are mentioned only once. The authors ask whether such one-off mentions (singletons) can be told apart from mentions that join a coreference chain, using only properties of the noun phrase and its context. They use the CoNLL-2012 Shared Task portion of OntoNotes v5.0. Because…
8. **[Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding](https://md.research.elamur.ai/free/en/papers/monroe-2017-colors-context-pragmatic-neural-model/)** *(2017 · 156 citations)* The paper asks how a system can interpret color descriptions as people do, taking into account which other colors appear alongside the target.
9. **[A computational approach to politeness with application to social factors](https://md.research.elamur.ai/free/en/papers/mizil-2013-computational-approach-politeness-application-social/)** *(2013 · 141 citations)* The paper addresses how to detect politeness in text automatically. It focuses on requests, because asking something of another person is an imposition and tends to trigger politeness strategies. The authors collected requests from Wikipedia user talk-pages and Stack Exchange comments. They had a subset rated on…
10. **[Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation](https://md.research.elamur.ai/free/en/papers/geiger-2020-neural-natural-language-inference-models/)** *(2020 · 123 citations)* The question is whether neural NLI models learn how negation reverses lexical entailment: "dance" entails "move", but "not move" does not entail "not dance".
11. **[Relevance-guided Supervision for OpenQA with ColBERT](https://md.research.elamur.ai/free/en/papers/khattab-2021-relevance-guided-supervision-openqa-colbert/)** *(2021 · 121 citations)* Open-domain question answering (OpenQA) systems pair a retriever with a reader. Learned retrievers such as DPR, ORQA and REALM compress each passage and question into one vector. The authors argue this is too coarse for complex questions, and that current supervision is costly or brittle. Some methods need…
12. **[Pragmatically Informative Image Captioning with Character-Level Inference](https://md.research.elamur.ai/free/en/papers/gordon-2018-pragmatically-informative-image-captioning-character/)** *(2018 · 103 citations)* Standard neural image captioners describe an image accurately but often fail to say what sets it apart from similar pictures.
13. **[Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs](https://md.research.elamur.ai/free/en/papers/ong-2024-optimizing-instructions-demonstrations-multi-stage/)** *(2024 · 99 citations)* Pipelines that chain several language model calls (LM programs) need prompts that work well together across all stages.
14. **[Interpretability at Scale: Identifying Causal Mechanisms in Alpaca](https://md.research.elamur.ai/free/en/papers/wu-2023-interpretability-scale-identifying-causal-mechanisms/)** *(2023 · 81 citations)* Causal-abstraction methods test whether a neural network actually implements a human-readable algorithm, but until now they had only been applied to small, task-specific models.
15. **[Learning in the Rational Speech Acts Model](https://md.research.elamur.ai/free/en/papers/monroe-2015-learning-rational-speech-acts-model/)** *(2015 · 78 citations)* The Rational Speech Acts (RSA) model describes pragmatics: the speaker and the listener recursively model each other's intentions and thereby enrich the literal meaning of utterances.
16. **[Mission: Impossible Language Models](https://md.research.elamur.ai/free/en/papers/kallini-2024-mission-impossible-language-models/)** *(2024 · 70 citations)* Chomsky and colleagues have argued that LLMs pick up languages humans could never acquire just as readily as natural ones, but almost no experiments have tested this.
17. **[Posing Fair Generalization Tasks for Natural Language Inference](https://md.research.elamur.ai/free/en/papers/geiger-2019-posing-fair-generalization-tasks-natural/)** *(2019 · 61 citations)* Adversarial and synthetic test sets often show that NLI models with strong scores on naturalistic corpora break down on systematic cases.
18. **[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 · 56 citations)* 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…
19. **[Representing Social Media Users for Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/kolchinski-2018-representing-social-media-users-sarcasm/)** *(2018 · 54 citations)* Sarcasm is hard to detect from text alone because the same words can be literal or ironic depending on context.
20. **[Emergence of Gricean Maxims from Multi-Agent Decision Theory](https://md.research.elamur.ai/free/en/papers/vogel-2013-emergence-gricean-maxims-multi-agent/)** *(2013 · 51 citations)* Grice's cooperative principle and maxims (quality, relation, quantity, manner) are usually stated as norms.
21. **[Implicatures and Nested Beliefs in Approximate Decentralized-POMDPs](https://md.research.elamur.ai/free/en/papers/vogel-2013-implicatures-nested-beliefs-approximate-decentralized/)** *(2013 · 45 citations)* Gricean implicatures require agents to reason about what others believe about their own beliefs. Such nested reasoning is hard to model computationally. Vogel, Potts and Jurafsky treat communication as a decision problem inside a Decentralized-POMDP (Dec-POMDP), in which two agents share a reward. Exact Dec-POMDP…
22. **[CausalGym: Benchmarking causal interpretability methods on linguistic tasks](https://md.research.elamur.ai/free/en/papers/arora-2024-causalgym-benchmarking-causal-interpretability-methods/)** *(2024 · 38 citations)* CausalGym connects two research lines that have mostly stayed apart. Psycholinguistic work tests language models on large behavioural benchmarks. Interpretability work tries to find internal causal mechanisms but usually evaluates on small, one-off datasets. The authors turn SyntaxGym test suites into templates that…
23. **[Stress-Testing Neural Models of Natural Language Inference with Multiply-Quantified Sentences](https://md.research.elamur.ai/free/en/papers/geiger-2018-stress-testing-neural-models-natural/)** *(2018 · 37 citations)* Benchmarks built from naturalistic NLI corpora say little about how much compositional meaning a model actually captures, because their examples have no reliable measure of semantic difficulty.
24. **[An Incremental Iterated Response Model of Pragmatics](https://md.research.elamur.ai/free/en/papers/gordon-2019-incremental-iterated-response-model-pragmatics/)** *(2019 · 35 citations)* Rational Speech Acts (RSA) and other Iterated Response (IR) models treat pragmatics as speakers and listeners recursively reasoning about one another.
25. **[Rigorously Assessing Natural Language Explanations of Neurons](https://md.research.elamur.ai/free/en/papers/huang-2023-rigorously-assessing-natural-language-explanations/)** *(2023 · 30 citations)* Natural-language descriptions of what individual neurons in large language models encode are appealing, but it is unclear whether they faithfully reflect model behavior.

**1** [2](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/page/2/) [next →](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/page/2/)

## All works

270 works of theirs in our library, the most cited first; 33 with a page here, the others link their original until we summarise them.

1. **[Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank](https://md.research.elamur.ai/free/en/papers/socher-2013-recursive-deep-models-semantic-compositionality/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Richard Socher, Alex Perelygin, Jean Wu, et al.*2013 · 9,138 citations*
2. **[A large annotated corpus for learning natural language inference](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Samuel R. Bowman, Gabor Angeli, Christopher Potts, et al.*2015 · 5,299 citations*
3. **[Learning Word Vectors for Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/maas-2011-learning-word-vectors-sentiment-analysis/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Andrew L. Maas, Raymond E. Daly, Peter T. Pham, et al.*2011 · 4,333 citations*
4. **[The Logic of Conventional Implicatures ↗](https://doi.org/10.1093/acprof:oso/9780199273829.001.0001) Oxford University Press (book) · no summary yet**Christopher Potts*2005 · 1,401 citations*
5. **[A logic for conventional implicatures ↗](https://doi.org/10.1093/acprof:oso/9780199273829.003.0003) Oxford University Press eBooks · no summary yet**Christopher E. Potts*2004 · 908 citations*
6. **[The expressive dimension ↗](https://doi.org/10.1515/tl.2007.011) Theoretical Linguistics · no summary yet**Christopher Potts*2007 · 789 citations*
7. **[ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://md.research.elamur.ai/free/en/papers/santhanam-2022-colbertv2-effective-efficient-retrieval-lightweight/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, et al.*2022 · 485 citations*
8. **[Dynabench: Rethinking Benchmarking in NLP ↗](https://aclanthology.org/2021.naacl-main.324/) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · no summary yet**Douwe Kiela et al.*2021 · 482 citations*
9. **[No country for old members ↗](https://doi.org/10.1145/2488388.2488416) International World Wide Web Conference (WWW) · no summary yet**Cristian Danescu-Niculescu-Mizil et al.*2013 · 342 citations*
10. **[A Fast Unified Model for Parsing and Sentence Understanding ↗](https://aclanthology.org/P16-1139/) Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yet**Samuel R. Bowman et al.*2016 · 323 citations*
11. **[Perspective-shifting with appositives and expressives ↗](https://doi.org/10.1007/s10988-010-9070-5) Linguistics and Philosophy · no summary yet**Jesse A. Harris, Christopher E. Potts*2009 · 263 citations*
12. **[Exploiting Social Network Structure for Person-to-Person Sentiment Analysis ↗](https://aclanthology.org/Q14-1024/) Transactions of the Association for Computational Linguistics · no summary yet**Robert West et al.*2014 · 224 citations*
13. [**Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP arXiv:2212.14024**Omar Khattab, Keshav Santhanam, Xiang Lisa Li, et al.*2022 · 222 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2022-demonstrate-search-predict-composing-retrieval/)
14. **[Did It Happen? The Pragmatic Complexity of Veridicality Assessment](https://md.research.elamur.ai/free/en/papers/marneffe-2012-did-it-happen-pragmatic-complexity/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Marie-Catherine de Marneffe, Christopher D. Manning, Christopher Potts*2012 · 202 citations*
15. **[MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions ↗](https://aclanthology.org/2023.emnlp-main.971/) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yet**Zexuan Zhong et al.*2023 · 199 citations*
16. **[Affective Demonstratives and the Division of Pragmatic Labor ↗](https://doi.org/10.1007/978-3-642-14287-1_5) Lecture notes in computer science · no summary yet**Christopher Michael Davis, Christopher E. Potts*2010 · 195 citations*
17. **[ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems ↗](https://aclanthology.org/2024.naacl-long.20/) Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · no summary yet**Jon Saad-Falcon et al.*2024 · 191 citations*
18. **[Crowdsourcing and language studies: the new generation of linguistic data ↗](https://aclanthology.org/W10-0719/) Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk · no summary yet**Robert Munro et al.*2010 · 179 citations*
19. **[Recursive Neural Networks Can Learn Logical Semantics ↗](https://aclanthology.org/W15-4002/) Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality · no summary yet**Samuel R. Bowman, Christopher Potts, Christopher D. Manning*2015 · 167 citations*
20. **[The Life and Death of Discourse Entities: Identifying Singleton Mentions](https://md.research.elamur.ai/free/en/papers/recasens-2013-life-death-discourse-entities-identifying/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Marta Recasens, Marie-Catherine de Marneffe, Christopher Potts*2013 · 166 citations*
21. **[The syntax and semantics of As-parentheticals ↗](https://doi.org/10.1023/a:1015892718818) Natural Language & Linguistic Theory · no summary yet**Christopher E. Potts*2002 · 159 citations*
22. **[Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding](https://md.research.elamur.ai/free/en/papers/monroe-2017-colors-context-pragmatic-neural-model/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Will Monroe, Robert X.D. Hawkins, Noah D. Goodman, et al.*2017 · 156 citations*
23. **[The Pragmatic Values of Evidential Sentences ↗](https://doi.org/10.3765/salt.v17i0.2966) Proceedings from Semantics and Linguistic Theory · no summary yet**Christopher Michael Davis, Christopher E. Potts, Margaret Speas*2007 · 154 citations*
24. **[Causal Abstractions of Neural Networks ↗](https://arxiv.org/abs/2106.02997) arXiv:2106.02997 · no summary yet**Atticus Geiger et al.*2021 · 151 citations*
25. **[A computational approach to politeness with application to social factors](https://md.research.elamur.ai/free/en/papers/mizil-2013-computational-approach-politeness-application-social/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Cristian Danescu-Niculescu-Mizil, Moritz Sudhof, Dan Jurafsky, et al.*2013 · 141 citations*

[See all 270 works by Christopher E. Potts →](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/works/)

## Co-authors here

12 of 41 co-authors with a page, the most papers together first.

- [Christopher D. Manning](https://md.research.elamur.ai/free/en/authors/christopher-d-manning/) ×5
- [Noah D. Goodman](https://md.research.elamur.ai/free/en/authors/noah-d-goodman/) ×5
- [Atticus Geiger](https://md.research.elamur.ai/free/en/authors/atticus-geiger/) ×4
- [Dan Jurafsky](https://md.research.elamur.ai/free/en/authors/dan-jurafsky/) ×4
- [Karel D'Oosterlinck](https://md.research.elamur.ai/free/en/authors/karel-d-oosterlinck/) ×4
- [Marie-Catherine de Marneffe](https://md.research.elamur.ai/free/en/authors/marie-catherine-de-marneffe/) ×4
- [Matei Zaharia](https://md.research.elamur.ai/free/en/authors/matei-zaharia/) ×4
- [Omar Khattab](https://md.research.elamur.ai/free/en/authors/omar-khattab/) ×4
- [Reuben Cohn-Gordon](https://md.research.elamur.ai/free/en/authors/reuben-cohn-gordon/) ×3
- [Adam Vogel](https://md.research.elamur.ai/free/en/authors/adam-vogel/) ×2
- [Andrew Ng](https://md.research.elamur.ai/free/en/authors/andrew-ng/) ×2
- [Chris Develder](https://md.research.elamur.ai/free/en/authors/chris-develder/) ×2

## Venues

Where their works here were published, the most works first, with the editions of those that have a page.

- [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) ×16: [EMNLP 2025](https://md.research.elamur.ai/free/en/venues/emnlp/2025/), [EMNLP 2024](https://md.research.elamur.ai/free/en/venues/emnlp/2024/), [EMNLP 2019](https://md.research.elamur.ai/free/en/venues/emnlp/2019/), [EMNLP 2018](https://md.research.elamur.ai/free/en/venues/emnlp/2018/), [EMNLP 2015](https://md.research.elamur.ai/free/en/venues/emnlp/2015/), [EMNLP 2013](https://md.research.elamur.ai/free/en/venues/emnlp/2013/)
- [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×11: [ACL 2024](https://md.research.elamur.ai/free/en/venues/acl/2024/) ×2, [ACL 2013](https://md.research.elamur.ai/free/en/venues/acl/2013/) ×2, [ACL 2011](https://md.research.elamur.ai/free/en/venues/acl/2011/), [ACL 2010](https://md.research.elamur.ai/free/en/venues/acl/2010/)
- Oxford University Press eBooks ×8
- Proceedings from Semantics and Linguistic Theory ×8
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) ×7
- Stanford Digital Repository ×6
- [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/) ×5: [Transactions of the Association for Computational Linguistics (TACL) 2025](https://md.research.elamur.ai/free/en/venues/tacl/2025/), [Transactions of the Association for Computational Linguistics (TACL) 2021](https://md.research.elamur.ai/free/en/venues/tacl/2021/), [Transactions of the Association for Computational Linguistics (TACL) 2017](https://md.research.elamur.ai/free/en/venues/tacl/2017/)
- [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/) ×4: [NAACL 2022](https://md.research.elamur.ai/free/en/venues/naacl/2022/), [NAACL 2018](https://md.research.elamur.ai/free/en/venues/naacl/2018/), [NAACL 2013](https://md.research.elamur.ai/free/en/venues/naacl/2013/) ×2
- Findings of the Association for Computational Linguistics: EMNLP 2025 ×4
- Lecture notes in computer science ×4
- neural information processing systems ×4
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track ×3

## Cited by

Papers of our library cite their works 11,943 times (of the 149,439 papers whose bibliographies we read); [1,666 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/cited-by/).

Updated 30 October 2026