Sophia Yat Mei Lee

Also published as: Yat-Mei Lee


2022

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One-Teacher and Multiple-Student Knowledge Distillation on Sentiment Classification
Xiaoqin Chang | Sophia Yat Mei Lee | Suyang Zhu | Shoushan Li | Guodong Zhou
Proceedings of the 29th International Conference on Computational Linguistics

Knowledge distillation is an effective method to transfer knowledge from a large pre-trained teacher model to a compacted student model. However, in previous studies, the distilled student models are still large and remain impractical in highly speed-sensitive systems (e.g., an IR system). In this study, we aim to distill a deep pre-trained model into an extremely compacted shallow model like CNN. Specifically, we propose a novel one-teacher and multiple-student knowledge distillation approach to distill a deep pre-trained teacher model into multiple shallow student models with ensemble learning. Moreover, we leverage large-scale unlabeled data to improve the performance of students. Empirical studies on three sentiment classification tasks demonstrate that our approach achieves better results with much fewer parameters (0.9%-18%) and extremely high speedup ratios (100X-1000X).

2020

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An Event-comment Social Media Corpus for Implicit Emotion Analysis
Sophia Yat Mei Lee | Helena Yan Ping Lau
Proceedings of the Twelfth Language Resources and Evaluation Conference

The classification of implicit emotions in text has always been a great challenge to emotion processing. Even though the majority of emotion expressed implicitly, most previous attempts at emotions have focused on the examination of explicit emotions. The poor performance of existing emotion identification and classification models can partly be attributed to the disregard of implicit emotions. In view of this, this paper presents the development of a Chinese event-comment social media emotion corpus. The corpus deals with both explicit and implicit emotions with more emphasis being placed on the implicit ones. This paper specifically describes the data collection and annotation of the corpus. An annotation scheme has been proposed for the annotation of emotion-related information including the emotion type, the emotion cause, the emotion reaction, the use of rhetorical question, the opinion target (i.e. the semantic role in an event that triggers an emotion), etc. Corpus data shows that the annotated items are of great value to the identification of implicit emotions. We believe that the corpus will be a useful resource for both explicit and implicit emotion classification and detection as well as event classification.

2018

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Exclamative Sentences in Emotion Expressions in Mandarin Chinese: A Corpus-based Approach
Xuefeng Gao | Sophia Yat Mei Lee
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

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Questions as a Pre-event, Pivot Event and Post-event of Emotions
Helena Yan Ping Lau | Sophia Yat Mei Lee | Zhongqing Wang
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

2017

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A Corpus-based Analysis of Near-Synonymous Sentence-final Particles in Mandarin Chinese: “bale” and “eryi”
Xuefeng Gao | Yat-Mei Lee
Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation

2015

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A Comparative Study on Mandarin and Cantonese Resultative Verb Compounds
Helena Yan Ping Lau | Sophia Yat Mei Lee
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

2010

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Employing Personal/Impersonal Views in Supervised and Semi-Supervised Sentiment Classification
Shoushan Li | Chu-Ren Huang | Guodong Zhou | Sophia Yat Mei Lee
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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A Text-driven Rule-based System for Emotion Cause Detection
Sophia Yat Mei Lee | Ying Chen | Chu-Ren Huang
Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text

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Textual Emotion Processing From Event Analysis
Chu-Ren Huang | Ying Chen | Sophia Yat Mei Lee
CIPS-SIGHAN Joint Conference on Chinese Language Processing

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Emotion Cause Events: Corpus Construction and Analysis
Sophia Yat Mei Lee | Ying Chen | Shoushan Li | Chu-Ren Huang
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Emotion processing has always been a great challenge. Given the fact that an emotion is triggered by cause events and that cause events are an integral part of emotion, this paper constructs a Chinese emotion cause corpus as a first step towards automatic inference of cause-emotion correlation. The corpus focuses on five primary emotions, namely happiness, sadness, fear, anger, and surprise. It is annotated with emotion cause events based on our proposed annotation scheme. Corpus data shows that most emotions are expressed with causes, and that causes mostly occur before the corresponding emotion verbs. We also examine the correlations between emotions and cause events in terms of linguistic cues: causative verbs, perception verbs, epistemic markers, conjunctions, prepositions, and others. Results show that each group of linguistic cues serves as an indicator marking the cause events in different structures of emotional constructions. We believe that the emotion cause corpus will be the useful resource for automatic emotion cause detection as well as emotion detection and classification.

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Emotion Cause Detection with Linguistic Constructions
Ying Chen | Sophia Yat Mei Lee | Shoushan Li | Chu-Ren Huang
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

2009

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Cause Event Representations for Happiness and Surprise
Sophia Yat Mei Lee | Ying Chen | Chu-Ren Huang
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1