Automated Phrase Mining from Massive Text Corpora
By Jingbo Shang, Jialu Liu, Meng Jiang, Xiang Ren, Clare R. Voss, Jiawei Han. IEEE Transactions on Knowledge and Data Engineering, 2017
322 citations recorded by the source corpus.
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 extraction/retrieval, 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, is 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, <inline-formula> <tex-math notation="LaTeX">$\mathsf{AutoPhrase}$</tex-math><alternatives> <inline-graphic xlink:href="shang-ieq1-2812203.gif"/></alternatives></inline-formula>, 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, <inline-formula><tex-math notation="LaTeX"> $\mathsf{AutoPhrase}$</tex-math><alternatives><inline-graphic xlink:href="shang-ieq2-2812203.gif"/></alternatives> </inline-formula> has shown significant improvements in both effectiveness and efficiency on five real-world datasets across different domains and languages. Besides, <inline-formula><tex-math notation="LaTeX">$\mathsf{AutoPhrase}$ </tex-math><alternatives><inline-graphic xlink:href="shang-ieq3-2812203.gif"/></alternatives></inline-formula> can be extended to model single-word quality phrases.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/96808500be49f3d502055bab1edd30dcbec4b99b
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Citations in the corpus
- Empower Sequence Labeling with Task-Aware Neural Language Model (2017, methodology): “ to a concise, effective, and efficient neural network. Besides, unlike other multi-task learning methods, our model has no reliance on any extra annotation (Peters et al. 2017) or any knowledge base (Shang et al. 2017). Instead, it extracts knowledge from the self-contained order information. Conclu”
- AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging (2019, methodology): “Specifically, after admins upload raw corpora, AlpacaTag runs a phrase mining algorithm (Shang et al., 2018) to gather a list of frequent noun phrases, which are more likely to be entities.”
- AliCoCo: Alibaba E-commerce Cognitive Concept Net (2020, methodology): “In practice, we adopt AutoPhrase[25] to mine possible concept phrases from large corpora in e-commerce including search queries, product titles, user-written reviews and shopping guidance written by merchants.”