Summary of the paper
| Title | Mining Wikipedia for Large-scale Repositories of Context-Sensitive Entailment Rules |
|---|---|
| Authors | Milen Kouylekov, Yashar Mehdad and Matteo Negri |
| Abstract | This paper focuses on the central role played by lexical information in the task of Recognizing Textual Entailment. In particular, the usefulness of lexical knowledge extracted from several widely used static resources, represented in the form of entailment rules, is compared with a method to extract lexical information from Wikipedia as a dynamic knowledge resource. The proposed acquisition method aims at maximizing two key features of the resulting entailment rules: coverage (i.e. the proportion of rules successfully applied over a dataset of TE pairs), and context sensitivity (i.e. the proportion of rules applied in appropriate contexts). Evaluation results show that Wikipedia can be effectively used as a source of lexical entailment rules, featuring both higher coverage and context sensitivity with respect to other resources. |
| Topics | Textual Entailment and Paraphrasing, Tools, systems, applications, Knowledge Discovery/Representation |
Full paper |
Mining Wikipedia for Large-scale Repositories of Context-Sensitive Entailment Rules |
Slides |
Mining Wikipedia for Large-scale Repositories of Context-Sensitive Entailment Rules |
| Bibtex | @InProceedings{KOUYLEKOV10.425, author = {Milen Kouylekov and Yashar Mehdad and Matteo Negri}, title = {Mining Wikipedia for Large-scale Repositories of Context-Sensitive Entailment Rules}, booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |