Chun-Hsun Chen


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預訓練詞向量模型應用於客服對話系統意圖偵測之研究(Study on Pre-trained Word Vector Model Applied to Intent Detection of Customer Service Dialogue System)
Guan-Yu Chen | Min-Feng Kuo | Tsung-Hsien Yang | Chun-Hsun Chen | I-Bin Liao
Proceedings of the 31st Conference on Computational Linguistics and Speech Processing (ROCLING 2019)


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A Telecom-Domain Online Customer Service Assistant Based on Question Answering with Word Embedding and Intent Classification
Jui-Yang Wang | Min-Feng Kuo | Jen-Chieh Han | Chao-Chuang Shih | Chun-Hsun Chen | Po-Ching Lee | Richard Tzong-Han Tsai
Proceedings of the IJCNLP 2017, System Demonstrations

In the paper, we propose an information retrieval based (IR-based) Question Answering (QA) system to assist online customer service staffs respond users in the telecom domain. When user asks a question, the system retrieves a set of relevant answers and ranks them. Moreover, our system uses a novel reranker to enhance the ranking result of information retrieval. It employs the word2vec model to represent the sentences as vectors. It also uses a sub-category feature, predicted by the k-nearest neighbor algorithm. Finally, the system returns the top five candidate answers, making online staffs find answers much more efficiently.


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Sentence Rephrasing for Parsing Sentences with OOV Words
Hen-Hsen Huang | Huan-Yuan Chen | Chang-Sheng Yu | Hsin-Hsi Chen | Po-Ching Lee | Chun-Hsun Chen
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper addresses the problems of out-of-vocabulary (OOV) words, named entities in particular, in dependency parsing. The OOV words, whose word forms are unknown to the learning-based parser, in a sentence may decrease the parsing performance. To deal with this problem, we propose a sentence rephrasing approach to replace each OOV word in a sentence with a popular word of the same named entity type in the training set, so that the knowledge of the word forms can be used for parsing. The highest-frequency-based rephrasing strategy and the information-retrieval-based rephrasing strategy are explored to select the word to replace, and the Chinese Treebank 6.0 (CTB6) corpus is adopted to evaluate the feasibility of the proposed sentence rephrasing strategies. Experimental results show that rephrasing some specific types of OOV words such as Corporation, Organization, and Competition increases the parsing performances. This methodology can be applied to domain adaptation to deal with OOV problems.