Please use this identifier to cite or link to this item: https://doi.org/10.1145/3397271.3401107
Title: Enhancing Text Classification via Discovering Additional Semantic Clues from Logograms
Authors: Chen Qian
FENG FULI 
Lijie Wen
Li Lin
CHUA TAT SENG 
Keywords: Bilingual Text Classification
Writing System
Logogram
Machine Translation
Gaussian Embedding
Issue Date: 25-Jul-2020
Publisher: SIGIR 2020
Citation: Chen Qian, FENG FULI, Lijie Wen, Li Lin, CHUA TAT SENG (2020-07-25). Enhancing Text Classification via Discovering Additional Semantic Clues from Logograms. SIGIR 2020. ScholarBank@NUS Repository. https://doi.org/10.1145/3397271.3401107
Rights: Attribution 4.0 International
Abstract: Text classification in low-resource languages (e.g., Thai) is of great practical value for some information retrieval applications (e.g., sentiment-analysis-based restaurant recommendation). Due to lacking large-scale corpus for learning comprehensive text representation, bilingual text classification which borrows the linguistics knowledge from a rich-resource language becomes a promising solution. Despite the success of bilingual methods, they largely ignore another source of semantic information—the writing system. Noting that most low-resource languages are phonographic languages, we argue that a logographic language (e.g., Chinese) can provide helpful information for improving some phonographic languages’ text classification, since a logographic character (i.e., logogram) could represent a sememe or a whole concept, not only a phoneme or a sound. In this paper, by using a phonographic labeled corpus and its machine-translated logographic corpus both, we devise a framework to explore the central theme of utilizing logograms as a “semantic detection assistant”. Specifically, from a logographic labeled corpus, we first devise a statistical-significance-based module to pick out informative text pieces. To represent them and further reduce the effects of translation errors, our approach is equipped with Gaussian embedding whose covariances serve as reliable signals of translation errors. For a test document, all seeds’ Gaussian representations are used to convolute the document and produce a logographic embedding, before being fused with its phonographic embedding for final prediction. Extensive experiments validate the effectiveness of our approach and further investigations show its generalizability and robustness.
Source Title: SIGIR 2020
URI: https://scholarbank.nus.edu.sg/handle/10635/183428
DOI: 10.1145/3397271.3401107
Rights: Attribution 4.0 International
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