Paper Contents
Abstract
As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus and has various downstream applications including information extractionretrieval,taxonomy construction, and topic modeling. Most existing methods rely on complex, trained linguistic analyzers, and thus likely have unsatisfactory performance on text corpora of new domains and genres without extra but expensive adaption. None of the state of the-art models, even data-driven models,gobbing fully automated because they require human experts for designing rules or labeling phrases. In this paper, we propose a novel framework for automated phrase mining, Auto-phrase, which supports any language as long as a general knowledge base (e.g., Wikipedia) in that language is available, while benefiting from, but not requiring, a POS tagger. Compared to the state-of-the-art methods, Auto-phrase has shown significant improvements in both effectiveness and efficiency on five real-world datasets across different domains and languages. Besides, Auto-phrase can be extend to model single-phase. Keywords:- Automatic Phrase Mining, Phrase Mining, Distant Training, Part-of-Speech tag, Multiple Language
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Copyright © 2025 CH.MADHUMADHI . This is an open access article distributed under the Creative Commons Attribution License.
