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| "title": "Study on Pre-trained Word Vector Model Applied to Intent Detection of Customer Service Dialogue System", | |
| "authors": [ | |
| { | |
| "first": "Guan-Yu", | |
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| "institution": "Chunghwa Telecom Co., Ltd", | |
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| "country": "Taiwan, R.O.C" | |
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| "country": "Taiwan, R.O.C" | |
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| "last": "Tsung-Hsien", | |
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| "institution": "Chunghwa Telecom Co., Ltd", | |
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| "country": "Taiwan, R.O.C" | |
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| { | |
| "first": "Yang", | |
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| "institution": "Chunghwa Telecom Co., Ltd", | |
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| "country": "Taiwan, R.O.C" | |
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| { | |
| "first": "Chun-Hsun", | |
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| "last": "Chen", | |
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| "institution": "Chunghwa Telecom Co., Ltd", | |
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| "country": "Taiwan, R.O.C" | |
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| { | |
| "first": "\u5ed6\u5b9c\u658c I-Bin", | |
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| "last": "Liao", | |
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| "institution": "Chunghwa Telecom Co., Ltd", | |
| "location": { | |
| "country": "Taiwan, R.O.C" | |
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| "year": "", | |
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| "abstract": "In recent years, the concept of dialogue business has arisen among major technology giants, and the way of human-computer interaction has changed from a graphical interface to a", | |
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| "text": "In recent years, the concept of dialogue business has arisen among major technology giants, and the way of human-computer interaction has changed from a graphical interface to a", | |
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| "body_text": [ | |
| { | |
| "text": "dialogue interaction interface. Therefore, natural language has become a key factor in the human-computer interaction interface. However, teaching the machine to communicate with humans to accomplish a specific task can be quite challenging. One of the difficulties that needs to overcome is natural language understanding, including how to identify what questions users are asking and how to get information hidden between words. It is important to let the machine know the user's intentions and information.", | |
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| "text": "The dataset of this study is collected from the dialogue of customer service materials. User's intents are recognized by deep learning models. In order to process Chinese unknown words more effectively and reduce false recognition, this study compares different pre-training vector models and deep learning models to understand user's intents. Compared with the use of random word embedding, the correct rate of using BERT-WWM-Chinese (BWC) model is improved by nearly 10%. It shows that the semantic vector generated by BWC model can better represent the relationship between user's words. The recognition rate of user's intent raises because similar vectors can be generated from similar words. ", | |
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| "text": "https://dumps.wikimedia.org/zhwiki/ 2 https://taku910.github.io/crfpp/", | |
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| "text": "https://ltp-cloud.com/", | |
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| "text": "https://radimrehurek.com/gensim/models/word2vec.html", | |
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| "authors": [ | |
| { | |
| "first": "S", | |
| "middle": [], | |
| "last": "Hochreiter", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "J", | |
| "middle": [], | |
| "last": "Schmidhuber", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1997, | |
| "venue": "Neural computation", | |
| "volume": "9", | |
| "issue": "8", | |
| "pages": "1735--1780", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "S. Hochreiter and J. Schmidhuber, \"Long short-term memory,\" Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.", | |
| "links": null | |
| } | |
| }, | |
| "ref_entries": {} | |
| } | |
| } |