Towards improving the performance of Vector Space Model for Chinese Frequently Asked Question Answering

Ridong Jiang, Seokhwan Kim, Rafael E. Banchs, Haizhou Li
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引用次数: 1

Abstract

This paper presents a method which improves the performance of Vector Space Model (VSM) when applying it to Chinese Frequently Asked Questions (FAQ). This method combines unigram and bigram models in determining the similarity of document vectors. The performance is further improved by applying shallow lexical semantics and the document length information. Experiments showed that the proposed methods outperform baselines (segmentation and bigram) across different datasets which include FAQs from restricted domains and open domains.
提高中文常见问题回答的向量空间模型的性能
本文提出了一种改进向量空间模型(VSM)在中文常见问题识别中的性能的方法。该方法结合单图和双图模型来确定文档向量的相似度。通过应用浅词法语义和文档长度信息,进一步提高了性能。实验表明,本文提出的方法在不同的数据集(包括来自限制域和开放域的常见问题)上优于基线(分割和双图)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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