Eyal Shnarch   -   אייל שנרך
Department of Computer Science, Bar Ilan University

[Contact | Publications | Demos & Downloads | Teaching | Talks ]

Eyal Shnarch Contact Information

Department of Computer Science
Bar Ilan University,
Ramat Gan, 52900, Israel		  
Phone: +972-3-5318054
Email: shey @ cs.biu.ac.il
How to pronounce my name
 

I have just submitted my PhD at the Department of Computer Science, Bar-Ilan University, under the supervision of Ido Dagan and Jacob Goldberger.
My research field is Natural Language Processing and I am a part of the NLP lab at Bar Ilan university. My main research interest is semantic Inference. I develop algorithms for the acqusition of lexical semantic relations, propose methods for the integration of knowledge reasources and build probabilistic models for inference at the lexical level.



Publications

  1. Eyal Shnarch, Erel Segal Halevi, Jacob Goldberger, Ido Dagan. PLIS: a Probabilistic Lexical Inference System. ACL (Demonstration), 2013. demo
  2. Eyal Shnarch, Ido Dagan, Jacob Goldberger. A Probabilistic Lexical Model for Ranking Textual Inferences. *SEM (Joint Conference on Lexical and Computational Semantics), 2012. PDF slides code
  3. Asher Stern, Amnon Lotan, Shachar Mirkin, Eyal Shnarch, Lili Kotlerman, Jonathan Berant, and Ido Dagan. Knowledge and Tree-Edits in Learnable Entailment Proofs. TAC, 2011. PDF
  4. Eyal Shnarch, Jacob Goldberger, Ido Dagan. Towards a Probabilistic Model for Lexical Entailment. TextInfer, 2011. PDF slides
  5. Eyal Shnarch, Jacob Goldberger, Ido Dagan. A Probabilistic Modeling Framework for Lexical Entailment. ACL, 2011. PDF
  6. Asher Stern, Eyal Shnarch, Amnon Lotan, Shachar Mirkin, Lili Kotlerman, Naomi Zeichner, Jonathan Berant, and Ido Dagan. Rule Chaining and Approximate Match in textual inference. TAC, 2010. PDF
  7. Shachar Mirkin, Jonathan Berant, Ido Dagan and Eyal Shnarch. Recognising Entailment within Discourse. COLING, 2010. PDF
  8. Shachar Mirkin, Roy Bar-Haim, Jonathan Berant, Ido Dagan, Eyal Shnarch, Asher Stern and Idan Szpektor. Addressing Discourse and Document Structure in the RTE Search Task. TAC, 2009. PDF
  9. Eyal Shnarch, Libby Barak, Ido Dagan. Extracting Lexical Reference Rules from Wikipedia. ACL, 2009. PDF slides db
  10. Libby Barak, Ido Dagan, Eyal Shnarch. Text Categorization from Category Name via Lexical Reference. NAACL, 2009. PDF
  11. Shachar Mirkin, Ido Dagan, Eyal Shnarch. Evaluating the Inferential Utility of Lexical-Semantic Resources. EACL, 2009. PDF
  12. Roy Bar-Haim, Jonathan Berant, Ido Dagan, Iddo Greental, Shachar Mirkin, Eyal Shnarch and Idan Szpektor. Efficient Semantic Deduction and Approximate Matching over Compact Parse Forests. TAC, 2008. PDF
  13. Idan Szpektor, Eyal Shnarch and Ido Dagan. Instance-based Evaluation of Entailment Rule Acquisition. ACL, 2007. PDF
  14. Roy Bar-Haim, Ido Dagan, Iddo Greental and Eyal Shnarch. Semantic Inference at the Lexical-Syntactic Level. AAAI, 2007. PDF
  15. Oren Glickman, Ido Dagan and Eyal Shnarch. Lexical Reference: a Semantic Matching Subtask. EMNLP, 2006. PDF


Demos & Downloads

  • PLIS: a Probabilistic Lexical Inference System
    Enter a Text and a Hypotheis, choose your knowledge resources, set a transitivity limit and get the entailment probability.
    The inference system is based on our line of reasearch including Shnarch et. al (2011a; 2011b; 2012, 2013).
    go to demo

  • M-PLM: a Probabilistic Lexical Model for Ranking Textual Inferences
    A Java package of the probabilistic model for textual inference, described in Shnarch et al. (2012), which uses lexical knowledge only.
    This model is the current back end of PLIS.
    More information and download.

  • Lexical entailment annotations
    Manuall annotations of the various lexical entailment phenomena in a sample from RTE-5 pilot task.
    We have randomly sampled text-hypothesis pairs from the development set, 100 from the positive examples and another 100 from the negative. Each (T,H) pair was manually annotated for all lexical entailments between terms of the text and terms of the hypothesis.
    More information and download.

  • WikiRules!
    A large scale lexical resource automatically extracted from Wikipedia.
    The algorithm to aoutomatically extract this rule-base from Wikipedia is described in Shnarch et. al (2009).
    Coming soon, WikiRules 2.0! A new and improved version of the rule base and the code to extract entailment rules from Wikipedia.
    More information and download.



Teaching



Talks

  • PLIS: a Probabilistic Lexical Inference System. HTL group at FBK, Trento Italy. August 2012. slides
  • Probabilistic Lexical Models for Textual Inference. Information Retrieval group at IBM Haifa Research Lab, Israel. July 2012. slides
  • A Probabilistic Model for Lexical Entailment. ISCOL. June 2011. slides
  • Extracting a Lexical Entailment Resource from Wikipedia. IAAI Symposium. June 2008. slides