---
title: "Ido Dagan — papers and cards"
description: "Ido Dagan: 5 paper cards in the Research library (2007–2020), with what each paper is about, the ideas Mova took from it, and the co-authors."
canonical: "https://research.elamur.ai/free/en/authors/ido-dagan/"
---
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# Ido Dagan

5 paper cards in the Research library · 2007–2020

Ido Dagan wrote 5 of the papers Mova reads, in 2007–2020, most of them on [Semantic role labeling](https://md.research.elamur.ai/free/en/topics/semantic-role-labeling/), [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/); often with [Luke Zettlemoyer](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/), [Ayal Klein](https://md.research.elamur.ai/free/en/authors/ayal-klein/), [Daniela Stepanov](https://md.research.elamur.ai/free/en/authors/daniela-stepanov/).

[OpenAlex](https://openalex.org/A5033006575) · [ORCID](https://orcid.org/0000-0003-3415-3374) · [**h-index 59**](https://openalex.org/authors/A5033006575) · [16076 citations](https://openalex.org/authors/A5033006575) · [427 works](https://openalex.org/works?filter=author.id:A5033006575) — 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=Ido+Dagan) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Ido+Dagan) · [dblp](https://dblp.org/search?q=Ido+Dagan) · [ACL Anthology](https://aclanthology.org/search/?q=Ido+Dagan) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Ido+Dagan)

Topics: [Semantic role labeling](https://md.research.elamur.ai/free/en/topics/semantic-role-labeling/) 3 · [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/) 1

## Papers

1. **[Controlled Crowdsourcing for High-Quality QA-SRL Annotation](https://md.research.elamur.ai/free/en/papers/roit-2020-controlled-crowdsourcing-high-quality-qa/)** *(2020)* The paper shows that the crowdsourced QA-SRL data of 2018 has low coverage, with an estimated recall in the low 70s, and proposes a controlled protocol to fix it.
2. **[QANom: Question-Answer driven SRL for Nominalizations](https://md.research.elamur.ai/free/en/papers/klein-2020-qanom-question-answer-driven-srl/)** *(2020)* QANom extends QA-SRL from verbs to nouns derived from verbs: a noun like proof or increase is questioned with its verb's question templates («What has someone proved?», «Why did something increase?») and answered with spans of the sentence.
3. **[Crowdsourcing Question-Answer Meaning Representations](https://md.research.elamur.ai/free/en/papers/michael-2018-crowdsourcing-question-answer-meaning-representations/)** *(2018)* A QAMR represents a sentence's predicate-argument structure as a set of free-form wh-question / answer-span pairs about all its content words, not only verbs («How old is Pierre Vinken? – 61 years old», «What will he join the board as? – nonexecutive director»).
4. **[Automatic Thesaurus Construction for Modern Hebrew](https://md.research.elamur.ai/free/en/papers/lingbulk-2018-liebeskind-automatic-thesaurus-construction-modern-hebrew/)** *(2018)* The authors build corpus-based thesauri for Modern Hebrew, a morphologically rich language where taggers and parsers are weak.
5. **[Semantic Inference at the Lexical-Syntactic Level for Textual Entailment Recognition](https://md.research.elamur.ai/free/en/papers/haim-2007-semantic-inference-lexical-syntactic-level/)** *(2007)* The paper proposes a proof system for textual entailment that works directly on dependency trees. Entailment rules, manual or acquired from WordNet and from DIRT-style paraphrase data, rewrite the text tree step by step towards the hypothesis, and a cost function measures the remaining distance when no full proof is…

## Co-authors (17)

- [Luke Zettlemoyer](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/) ×3
- [Ayal Klein](https://md.research.elamur.ai/free/en/authors/ayal-klein/) ×2
- [Daniela Stepanov](https://md.research.elamur.ai/free/en/authors/daniela-stepanov/) ×2
- [Gabriel Stanovsky](https://md.research.elamur.ai/free/en/authors/gabriel-stanovsky/) ×2
- [Jonathan Mamou](https://md.research.elamur.ai/free/en/authors/jonathan-mamou/) ×2
- [Julian Michael](https://md.research.elamur.ai/free/en/authors/julian-michael/) ×2
- [Chaya Liebeskind](https://md.research.elamur.ai/free/en/authors/chaya-liebeskind/)
- [Dan Roth](https://md.research.elamur.ai/free/en/authors/dan-roth/)
- Hangfeng He
- [Idan Szpektor](https://md.research.elamur.ai/free/en/authors/idan-szpektor/)
- Iddo Greental
- [Jonathan Schler](https://md.research.elamur.ai/free/en/authors/jonathan-schler/)
- [Luheng He](https://md.research.elamur.ai/free/en/authors/luheng-he/)
- Moshe Friedman
- Paul Roit
- [Roy Bar-Haim](https://md.research.elamur.ai/free/en/authors/roy-bar-haim/)
- [Valentina Pyatkin](https://md.research.elamur.ai/free/en/authors/valentina-pyatkin/)

## More papers in the library (18)

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

- [Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design](https://md.research.elamur.ai/free/en/papers/pyatkin-2023-design-choices-crowdsourcing-implicit-discourse/) — Valentina Pyatkin, Frances Yung, Merel C. J. Scholman, et al. · 2023
- [Cross-document Event Coreference Search: Task, Dataset and Modeling](https://md.research.elamur.ai/free/en/papers/eirew-2022-cross-document-event-coreference-search/) — Alon Eirew, Avi Caciularu, Ido Dagan · 2022
- [Design Choices in Crowdsourcing Discourse Relation Annotations: The Effect of Worker Selection and Training](https://md.research.elamur.ai/free/en/papers/scholman-2022-design-choices-crowdsourcing-discourse-relation/) — Merel Scholman, Valentina Pyatkin, Frances Yung, et al. · 2022
- [Cross-document Coreference Resolution over Predicted Mentions](https://md.research.elamur.ai/free/en/papers/cattan-2021-cross-document-coreference-resolution-over/) — Arie Cattan, Alon Eirew, Gabriel Stanovsky, et al. · 2021
- [Realistic Evaluation Principles for Cross-document Coreference Resolution](https://md.research.elamur.ai/free/en/papers/cattan-2021-realistic-evaluation-principles-cross-document/) — Arie Cattan, Alon Eirew, Gabriel Stanovsky, et al. · 2021
- [WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia](https://md.research.elamur.ai/free/en/papers/eirew-2021-wec-deriving-large-scale-cross/) — Alon Eirew, Arie Cattan, Ido Dagan · 2021
- [Paraphrasing vs Coreferring: Two Sides of the Same Coin](https://md.research.elamur.ai/free/en/papers/meged-2020-paraphrasing-vs-coreferring-two-sides/) — Yehudit Meged, Avi Caciularu, Vered Shwartz, et al. · 2020
- [QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines](https://md.research.elamur.ai/free/en/papers/pyatkin-2020-qadiscourse-discourse-relations-as-qa/) — Valentina Pyatkin, Ayal Klein, Reut Tsarfaty, et al. · 2020
- [Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference](https://md.research.elamur.ai/free/en/papers/falke-2019-ranking-generated-summaries-correctness-interesting/) — Tobias Falke, Leonardo F. R. Ribeiro, Prasetya Ajie Utama, et al. · 2019
- [Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution](https://md.research.elamur.ai/free/en/papers/barhom-2019-revisiting-joint-modeling-cross-document/) — Shany Barhom, Vered Shwartz, Alon Eirew, et al. · 2019
- [Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation](https://md.research.elamur.ai/free/en/papers/moryossef-2019-step-step-separating-planning-realization/) — Amit Moryossef, Yoav Goldberg, Ido Dagan · 2019
- [Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition](https://md.research.elamur.ai/free/en/papers/shwartz-2019-still-pain-neck-evaluating-text/) — Vered Shwartz, Ido Dagan · 2019
- [Improving Hypernymy Detection with an Integrated Path-based and Distributional Method](https://md.research.elamur.ai/free/en/papers/shwartz-2016-improving-hypernymy-detection-integrated-path/) — Vered Shwartz, Yoav Goldberg, Ido Dagan · 2016
- [context2vec: Learning Generic Context Embedding with Bidirectional LSTM](https://md.research.elamur.ai/free/en/papers/melamud-2016-context2vec-learning-generic-context-embedding/) — Oren Melamud, Jacob Goldberger, Ido Dagan · 2016
- [Do Supervised Distributional Methods Really Learn Lexical Inference Relations?](https://md.research.elamur.ai/free/en/papers/levy-2015-do-supervised-distributional-methods-really/) — Omer Levy, Steffen Remus, Chris Biemann, et al. · 2015
- [SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge](https://md.research.elamur.ai/free/en/papers/dzikovska-2013-semeval-2013-task-7-joint/) — Myroslava Dzikovska, Rodney Nielsen, Chris Brew, et al. · 2013
- [Generating Entailment Rules from FrameNet](https://md.research.elamur.ai/free/en/papers/aharon-2010-generating-entailment-rules-framenet/) — Roni Ben Aharon, Idan Szpektor, Ido Dagan · 2010
- [Automatic Processing of Large Corpora for the Resolution of Anaphora References](https://md.research.elamur.ai/free/en/papers/dagan-1990-automatic-processing-large-corpora-resolution/) — Ido Dagan, Alon Itai · 1990