• Automated Phrase Mining from Massive Text Corpora - Jingbo Shang

  • 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 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, AutoPhrase, 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, AutoPhrase has shown significant improvements in both effectiveness and efficiency on five real-world datasets across different domains and languages. Besides, AutoPhrase can be extend to model single-word quality phrases.
    Jingbo Shang is an Assistant Professor in Computer Science Engineering and Halıcıoğlu Data Science Institute at UC San Diego. He obtained his Ph.D. from Department of Computer Science, University of Illinois at Urbana-Champaign. He received his B.E. from Computer Science Department, Shanghai Jiao Tong University, China. His research focuses on mining and constructing structured knowledge from massive text corpora with minimum human effort. His research has been recognized by many prestigious awards, including Grand Prize of Yelp Dataset Challenge in 2015 and Google Ph.D. Fellowship in Structured Data and Database Management in 2017.