Ming Liao


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Coupling Global and Local Context for Unsupervised Aspect Extraction
Ming Liao | Jing Li | Haisong Zhang | Lingzhi Wang | Xixin Wu | Kam-Fai Wong
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Aspect words, indicating opinion targets, are essential in expressing and understanding human opinions. To identify aspects, most previous efforts focus on using sequence tagging models trained on human-annotated data. This work studies unsupervised aspect extraction and explores how words appear in global context (on sentence level) and local context (conveyed by neighboring words). We propose a novel neural model, capable of coupling global and local representation to discover aspect words. Experimental results on two benchmarks, laptop and restaurant reviews, show that our model significantly outperforms the state-of-the-art models from previous studies evaluated with varying metrics. Analysis on model output show our ability to learn meaningful and coherent aspect representations. We further investigate how words distribute in global and local context, and find that aspect and non-aspect words do exhibit different context, interpreting our superiority in unsupervised aspect extraction.


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ACE: Automatic Colloquialism, Typographical and Orthographic Errors Detection for Chinese Language
Shichao Dong | Gabriel Pui Cheong Fung | Binyang Li | Baolin Peng | Ming Liao | Jia Zhu | Kam-fai Wong
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

We present a system called ACE for Automatic Colloquialism and Errors detection for written Chinese. ACE is based on the combination of N-gram model and rule-base model. Although it focuses on detecting colloquial Cantonese (a dialect of Chinese) at the current stage, it can be extended to detect other dialects. We chose Cantonese becauase it has many interesting properties, such as unique grammar system and huge colloquial terms, that turn the detection task extremely challenging. We conducted experiments using real data and synthetic data. The results indicated that ACE is highly reliable and effective.

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Topic Extraction from Microblog Posts Using Conversation Structures
Jing Li | Ming Liao | Wei Gao | Yulan He | Kam-Fai Wong
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)