TY - JOUR AU - Gupta, Dishan AU - Carbonell, Jaime AU - Gershman, Anatole AU - Klein, Steve AU - Miller, David PY - 2015/02/19 Y2 - 2024/03/29 TI - Unsupervised Phrasal Near-Synonym Generation from Text Corpora JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 29 IS - 1 SE - Main Track: NLP and Machine Learning DO - 10.1609/aaai.v29i1.9504 UR - https://ojs.aaai.org/index.php/AAAI/article/view/9504 SP - AB - <p> 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. </p> ER -