Yi-Shin Chen


2021

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Unsupervised Multi-document Summarization for News Corpus with Key Synonyms and Contextual Embeddings
Yen-Hao Huang | Ratana Pornvattanavichai | Fernando Henrique Calderon Alvarado | Yi-Shin Chen
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

Information overload has been one of the challenges regarding information from the Internet. It is not a matter of information access, instead, the focus had shifted towards the quality of the retrieved data. Particularly in the news domain, multiple outlets report on the same news events but may differ in details. This work considers that different news outlets are more likely to differ in their writing styles and the choice of words, and proposes a method to extract sentences based on their key information by focusing on the shared synonyms in each sentence. Our method also attempts to reduce redundancy through hierarchical clustering and arrange selected sentences on the proposed orderBERT. The results show that the proposed unsupervised framework successfully improves the coverage, coherence, and, meanwhile, reduces the redundancy for a generated summary. Moreover, due to the process of obtaining the dataset, we also propose a data refinement method to alleviate the problems of undesirable texts, which result from the process of automatic scraping.

2019

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Discovering the Latent Writing Style from Articles: A Contextualized Feature Extraction Approach
Yen-Hao Huang | Ting-Wei Liu | Ssu-Rui Lee | Ya-Wen Yu | Wan-Hsuan Lee | Fernando Henrique Calderon Alvarado | Yi-Shin Chen
International Journal of Computational Linguistics & Chinese Language Processing, Volume 24, Number 1, June 2019

2018

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CARER: Contextualized Affect Representations for Emotion Recognition
Elvis Saravia | Hsien-Chi Toby Liu | Yen-Hao Huang | Junlin Wu | Yi-Shin Chen
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.

2014

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Multi-Lingual Sentiment Analysis of Social Data Based on Emotion-Bearing Patterns
Carlos Argueta | Yi-Shin Chen
Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP)

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Collaborative Ranking between Supervised and Unsupervised Approaches for Keyphrase Extraction
Gerardo Figueroa | Yi-Shin Chen
Proceedings of the 26th Conference on Computational Linguistics and Speech Processing (ROCLING 2014)