用对数线性模型进行问题分类

Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval Pub Date : 2006-08-06 DOI:10.1145/1148170.1148282

Phil Blunsom, K. Kocik, J. Curran

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引用次数: 66

摘要

问题分类已经成为现代问答系统中至关重要的一步。以前的工作已经证明了统计机器学习方法在这个问题上的有效性。本文提出了一种利用对数线性模型构建问题分类器的新方法。来自丰富多样的句法和语义特征的证据被评估，以及利用问题类的层次结构的方法。

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Question classification with log-linear models

Question classification has become a crucial step in modern question answering systems. Previous work has demonstrated the effectiveness of statistical machine learning approaches to this problem. This paper presents a new approach to building a question classifier using log-linear models. Evidence from a rich and diverse set of syntactic and semantic features is evaluated, as well as approaches which exploit the hierarchical structure of the question classes.

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Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

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