Unsupervised Phrasal Near-Synonym Generation from Text Corpora
| Autor: | Dishan Gupta, Jaime Carbonell, Anatole Gershman, Steve Klein, David Miller |
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| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | Proceedings of the AAAI Conference on Artificial Intelligence. 29 |
| ISSN: | 2374-3468 2159-5399 |
| DOI: | 10.1609/aaai.v29i1.9504 |
| Popis: | Unsupervised discovery of synonymous phrases is useful in a variety of tasks ranging from text mining and search engines to semantic analysis and machine translation. This paper presents an unsupervised corpus-based conditional model: Near-Synonym System (NeSS) for finding phrasal synonyms and near synonyms that requires only a large monolingual corpus. The method is based on maximizing information-theoretic combinations of shared contexts and is parallelizable for large-scale processing. An evaluation framework with crowd-sourced judgments is proposed and results are compared with alternate methods, demonstrating considerably superior results to the literature and to thesaurus look up for multi-word phrases. Moreover, the results show that the statistical scoring functions and overall scalability of the system are more important than language specific NLP tools. The method is language-independent and practically useable due to accuracy and real-time performance via parallel decomposition. |
| Databáze: | OpenAIRE |
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