Abstract
A diachronic thesaurus is a lexical resource that aims to map between modern terms and their semantically related terms in earlier periods. In this paper, we investigate the task of collecting a list of relevant modern target terms for a domain-specific diachronic thesaurus. We propose a supervised learning scheme, which integrates features from two closely related fields: Terminology Extraction and Query Performance Prediction (QPP). Our method further expands modern candidate terms with ancient related terms, before assessing their corpus relevancy with QPP measures. We evaluate the empirical benefit of our method for a thesaurus for a diachronic Jewish corpus. c 2015 Association for Computational Linguistics and The Asian Federation of Natural Language Processing.
| Original language | English |
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| Title of host publication | LaTeCH 2015 - Proceedings of the 9th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities |
| Editors | Kalliopi A. Zervanou, Marieke van Erp, Beatrice Alex |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 89-94 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781941643631 |
| State | Published - 2015 |
| Event | 9th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, LaTeCH 2015 - Beijing, China Duration: 30 Jul 2015 → … |
Publication series
| Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
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| Volume | 2015-text |
| ISSN (Print) | 0736-587X |
Conference
| Conference | 9th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, LaTeCH 2015 |
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| Country/Territory | China |
| City | Beijing |
| Period | 30/07/15 → … |
Bibliographical note
Publisher Copyright:© 2015 Proceedings of the Annual Meeting of the Association for Computational Linguistics.