Summary of the paper
| Title | Boosting Precision and Recall of Hyponymy Relation Acquisition from Hierarchical Layouts in Wikipedia |
|---|---|
| Authors | Asuka Sumida, Naoki Yoshinaga and Kentaro Torisawa |
| Abstract | This paper proposes an extension of Sumida and Torisawa’s method of acquiring hyponymy relations from hierachical layouts in Wikipedia (Sumida and Torisawa, 2008). We extract hyponymy relation candidates (HRCs) from the hierachical layouts in Wikipedia by regarding all subordinate items of an item x in the hierachical layouts as x’s hyponym candidates, while Sumida and Torisawa (2008) extracted only direct subordinate items of an item x as x’s hyponym candidates. We then select plausible hyponymy relations from the acquired HRCs by running a filter based on machine learning with novel features, which even improve the precision of the resulting hyponymy relations. Experimental results show that we acquired more than 1.34 million hyponymy relations with a precision of 90.1%. |
| Language | Single language |
| Topics | Acquisition, Machine Learning, Lexicon, lexical database, Question Answering |
Full paper |
Boosting Precision and Recall of Hyponymy Relation Acquisition from Hierarchical Layouts in Wikipedia |
Slides |
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| Bibtex | @InProceedings{SUMIDA08.618, author = {Asuka Sumida, Naoki Yoshinaga and Kentaro Torisawa}, title = {Boosting Precision and Recall of Hyponymy Relation Acquisition from Hierarchical Layouts in Wikipedia}, booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)}, year = {2008}, month = {may}, date = {28-30}, address = {Marrakech, Morocco}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-4-0}, note = {http://www.lrec-conf.org/proceedings/lrec2008/}, language = {english} } |