Idan

Idan Szpektor - Home Page

I am a PhD student of Computer Science, under the supervision of Ido Dagan at Bar-Ilan University.
My main research interest is Natural Language Processing. My current research is on Textual Entailment.


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You can also read my blog or my LinkedIn profile.

Check out the Wiki site of Natural Language Processing in Israel.

My CV can be downloaded here.


Education

2005: Master of Science in Natural Language Processing (Summa Cum Laude). Tel Aviv University, Tel Aviv, Israel. Supervisors: Dr. Ido Dagan, Prof. Yossi Matias.

1997: Bachelor of Science in Mathematics and Computer Science (Magna Cum Laude). Tel Aviv University, Tel Aviv, Israel.


Publications

Shachar Mirkin, Lucia Specia, Nicola Cancedda, Ido Dagan, Marc Dymetman and Idan Szpektor. 2009. Source-Language Entailment Modeling for Translating Unknown Terms. Accepted as full paper to ACL-IJCNLP 2009.

Lili Kotlerman, Ido Dagan, Idan Szpektor and Maayan Zhitomirsky-Geffet. 2009. Directional Distributional Similarity for Lexical Expansion. Accepted as short paper to ACL-IJCNLP 2009.

Idan Szpektor and Ido Dagan. 2009. Augmenting WordNet-based Inference with Argument Mapping. Accepted as full paper to TextInfer 2009.

Idan Szpektor and Ido Dagan. 2008. Learning Entailment Rules for Unary Templates. In Proceedings of COLING 2008. (pdf)

Idan Szpektor, Ido Dagan, Roy Bar-Haim and Jacob Goldberger. 2008. Contextual Preferences. In Proceedings of ACL 2008. (pdf)

Roy Bar-Haim, Jonathan Berant, Ido Dagan, Iddo Greental, Shachar Mirkin, Eyal Shnarch and Idan Szpektor. 2008. Efficient Semantic Deduction and Approximate Matching over Compact Parse Forests. In Proceedings of Text Analysis Conference (TAC) 2008.

Ido Dagan, Roy Bar-Haim, Idan Szpektor, Iddo Greental, and Eyal Shnarch. 2008. Natural Language as the Basis for Meaning Representation and Inference. In Proceedings of CICLING 2008.

Idan Szpektor, Eyal Shnarch and Ido Dagan. 2007. Instance-based Evaluation of Entailment Rule Acquisition. In Proceedings of ACL 2007. (pdf)

Idan Szpektor and Ido Dagan. 2007. Learning Canonical Forms of Entailment Rules. In Proceedings of RANLP 2007. (pdf)

Idan Szpektor, Ido Dagan, Alon Lavie, Danny Shacham and Shuly Winter. 2007. Cross Lingual and Semantic Retrieval for Cultural Heritage Appreciation. In Proceedings of LaTeCH 2007. (pdf)

Roy Bar-Haim, Ido Dagan, Iddo Greental, Idan Szpektor and Moshe Fridman. 2007. Semantic Inference at the Lexical-Syntactic Level for Textual Entailment Recognition. In Proceedings of WTEP 2007, Prague. (pdf)

Roy Bar-Haim, Ido Dagan, Bill Dolan, Lisa Ferro, Danilo Giampiccolo, Bernardo Magnini and Idan Szpektor. 2006. The Second PASCAL Recognising Textual Entailment Challenge. In Proceedings of the Second PASCAL Recognising Textual Entailment Challenge. (pdf)

Lorenza Romano, Milen Kouylekov, Idan Szpektor, Ido Dagan and Alberto Lavelli. 2006. Investigating a Generic Paraphrase-based Approach for Relation Extraction. On Proceedings of EACL 2006. (pdf)

Roy Bar-Haim, Idan Szpektor and Oren Glickman. 2005. Definition and Analysis of Intermediate Entailment Levels. In Proceedings of ACL 2005 Workshop on Empirical Modeling of Semantic Equivalence and Entailment. (pdf)

Idan Szpektor. 2005. Scaling Web-based Acquisition of Entailment Relations, M.Sc. thesis, Tel-Aviv University, Department of Computer Science. (doc)

Idan Szpektor, Hristo Tanev, Ido Dagan and Bonaventura Coppola. 2004. Scaling Web-based Acquisition of Entailment Relations. In Proceedings of EMNLP 2004. (pdf)


Scholarships


Academic Activities

Invited Talks and Seminars

Others


Resources

TEASE is an algorithm for acquisition of entailment relations. Given a lexical-syntactic input template, a parse sub-tree with variable slots, the algorithm automatically learns other templates that are candidates for entailment relation with the input template. The direction of the entailment relation is not learned in this version, so the resulting relation can be either that the input entails the candidate, the candidate entails the input or both entail each other (paraphrases). The structure of the candidates is also learned as part of the acquisition.

The current knowledge collection available consists of 136 different templates that were given as input. Under each directory you can find two files: pivot.xml, containing the description of the input template, and learned_templates.xml, containing the description of all the learned templates for that input template. All the templates in this collection are learned from the Web.

The current TEASE knowledge collection is available for research purposes: TEASE collection.

Please refer to the following publication when using this resource:
Idan Szpektor, Hristo Tanev, Ido Dagan and Bonaventura Coppola. 2004. Scaling Web-based Acquisition of Entailment Relations. in Proceedings of EMNLP 2004.


Courses

Introduction to Communication Networks - Fall semester 2006-2007
Introduction to Communication Networks - Fall semester 2005-2006



Office address: Room 207, Department of Mathematics and Computer Science. Bar-Ilan University, Ramat Gan, 52900, Israel.
Phone: +972-3-531-8054

Backup web-site: http://idan.szpektor.googlepages.com/