---
title: "Who cites Computing relative polarity for textual inference"
description: "69 papers of the Elamur Research library cite «Computing relative polarity for textual inference», found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/papers/nairn-2006-computing-relative-polarity-textual-inference/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Computing relative polarity for textual inference](https://md.research.elamur.ai/free/en/papers/nairn-2006-computing-relative-polarity-textual-inference/) / cited by

# Who cites «Computing relative polarity for textual inference»

69 papers of our library cite it (of the 140,884 whose bibliographies we read) · 9 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/nairn-2006-computing-relative-polarity-textual-inference/)

1. [**Finding Contradictions in Text**Marie-Catherine de Marneffe, Anna N. Rafferty, Christopher D. Manning*2008 · 301 citations*](https://md.research.elamur.ai/free/en/papers/marneffe-2008-finding-contradictions-text/)
2. [**Neural Models of Factuality**Rachel Rudinger, Aaron Steven White, Benjamin Van Durme*2018 · 111 citations*](https://md.research.elamur.ai/free/en/papers/rudinger-2018-neural-models-factuality/)
3. [**Modality and Negation in SIMT Use of Modality and Negation in Semantically-Informed Syntactic MT**Kathryn Baker, Michael Bloodgood, Bonnie J. Dorr, et al.*2012 · 77 citations*](https://md.research.elamur.ai/free/en/papers/baker-2012-modality-negation-simt-use-modality/)
4. [**Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures**Amir Pouran Ben Veyseh, Thien Huu Nguyen, Dejing Dou*2019 · 69 citations*](https://md.research.elamur.ai/free/en/papers/veyseh-2019-graph-based-neural-networks-event/)
5. [**How well do NLI models capture verb veridicality?**Alexis Ross, Ellie Pavlick*2019 · 61 citations*](https://md.research.elamur.ai/free/en/papers/ross-2019-how-well-do-nli-models/)
6. [**Semantic Inference at the Lexical-Syntactic Level for Textual Entailment Recognition**Roy Bar-Haim, Ido Dagan, Iddo Greental, et al.*2007 · 39 citations*](https://md.research.elamur.ai/free/en/papers/haim-2007-semantic-inference-lexical-syntactic-level/)
7. [**He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics**Nanjiang Jiang, Marie-Catherine de Marneffe*2021 · 21 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2021-he-thinks-he-knows-better/)
8. [**Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations**Magdalena Markowska, Mohammad Taghizadeh, Adil Soubki, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/markowska-2023-finding-common-ground-annotating-predicting/)
9. [**Veridicity annotation in the lexicon? A look at factive adjectives**Annie Zaenen, Lauri Karttunen*2013 · 5 citations*](https://md.research.elamur.ai/free/en/papers/zaenen-2013-veridicity-annotation-lexicon-look-factive/)

60 more papers of our library cite it; they have no page here yet.

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