EMOVA : A Semi-supervised End-to-End Moving-Window Attentive Framework for Aspect Mining

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

2 Scopus Citations
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Author(s)

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Detail(s)

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings
EditorsHady W. Lauw, Raymond Chi-Wing Wong, Alexandros Ntoulas, Ee-Peng Lim, See-Kiong Ng, Sinno Jialin Pan
PublisherSpringer
Pages811-823
ISBN (Electronic)9783030474362
ISBN (Print)9783030474355
Publication statusPublished - 2020

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume12085
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020
PlaceSingapore
CitySingapore
Period11 - 14 May 2020

Abstract

Aspect mining or extraction is one of the most challenging problems in aspect-level analysis on customer reviews; it aims to extract terms from a review describing aspects of a reviewed entity, e.g., a product or service. As aspect mining can be formulated as the sequence labeling problem, supervised deep sequence learning models have recently achieved the best performance. However, these supervised models require a large amount of labeled data which are usually very costly or unavailable. To this end, we propose a semi-supervised End-to-end MOVing-window Attentive framework (called EMOVA) that has three key features for aspect mining. (1) Two neural layers with Bidirectional Long Short-Term Memory (BiLSTM) are employed to learn representations of reviews. (2) Cross-View Training (CVT) is used to improve the representation learning over a small set of labeled reviews and a large set of unlabeled reviews from the same domain in a unified end-to-end architecture. (3) Since past nearby information in a text provides important semantic contexts for a prediction task in aspect mining, a moving-window attention component is proposed in EMOVA to enhance prediction accuracy. Experimental results over four review datasets from the SemEval workshops show that EMOVA outperforms the state-of-the-art models for aspect mining.

Research Area(s)

  • Aspect mining, Semi-supervised learning, Cross-View training, Moving-window attention, End-to-end learning

Citation Format(s)

EMOVA : A Semi-supervised End-to-End Moving-Window Attentive Framework for Aspect Mining. / Li, Ning; Chow, Chi-Yin; Zhang, Jia-Dong.

Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings. ed. / Hady W. Lauw; Raymond Chi-Wing Wong; Alexandros Ntoulas; Ee-Peng Lim; See-Kiong Ng; Sinno Jialin Pan. Springer, 2020. p. 811-823 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 12085).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review