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首页> 美国政府科技报告 >Implementation and Performance Exploration of a Cross-Genre Part of Speech Tagging Methodology to Determine Dialog Act Tags in the Chat Domain
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Implementation and Performance Exploration of a Cross-Genre Part of Speech Tagging Methodology to Determine Dialog Act Tags in the Chat Domain

机译：用于确定聊天域中的对话行为标签的跨类型语音标记方法的实现和性能探索

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摘要

Internet Relay Chat is a popular means of communication. Because chat data does not follow established grammatical rules, traditional machine learning algorithms perform poorly in tasks such as part-of-speech and dialog- act tagging, and yet the volume of data created makes human analysis impractical. We present a cross-genre part-of-speech tagging methodology and analyze its effectiveness in determining the dialog-act classes of chat posts. Previous methods for determining part-of-speech tags focused on accuracy, were computationally expensive and required human verification. We show that our cross-genre maximum likelihood estimation part-of-speech tagging performs virtually identically to hand-tagged parts-of-speech and that accurate part-of- speech tags are not required for acceptable automatic dialog-act determination. Furthermore, we show that a simple naive Bayes classifier achieves the same performance in a fraction of the time as a carefully trained neural network.

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