基于改进TF-IDF和余弦相似度的自动问答

Muzamil Ahmed, H. Khan, Saqib Iqbal, Q. Althebyan
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引用次数: 1

摘要

提出了一种基于改进词频-逆文档频率(TF-IDF)和余弦相似度的自动问答系统。本研究的主要目的是提供一个有效的问答系统,能够以高置信度检索用户查询的精确且相关的答案。现有的相关文献研究表明,已经提出了几种用于自动问答系统的技术。基于规则的技术依赖于推理规则,响应用户查询所需的时间更少。然而,模式和推理规则的生成是困难的,因为自然语言缺乏遵循固定的模式。基于内容的相似性方法预先计算给定查询与所有存储库问题的相似性。在本研究中,首先对所有知识库问题进行预处理,并使用改进的TF -IDF模型生成一个矩阵。然后,通过查询预处理,找出每个用户查询与矩阵的相似度。对于所提出的方法,我们去除停止词,并应用词序化和词性标注技术进行预处理。提出的框架是使用现有研究中使用的标准数据集来实现的。基于实证分析的结果表明,采用该技术的系统在5秒内就能以最大的相似度响应用户的查询。该框架的准确率高达84%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated Question Answering based on Improved TF-IDF and Cosine Similarity
This paper proposes an automated question answering system based on improved Term Frequency- Inverse Document Frequency (TF-IDF) and cosine similarity. The main purpose of this research study is to provide an effective question answering system that retrieves precise and relevant answers to the users' queries with high confidence. The existing studies in the relevant literature show that several techniques have been proposed for automatic question answering systems. The rule-based techniques depend on inference rules and take less time to respond to the user query. However, generations of pattern and inference rules are difficult as natural languages lack to follow a fixed pattern. The content-based similarity method pre-computes similarity with all the repository questions for a given query. In this research study, firstly all repository questions are pre-processed and a matrix using the improved TF -IDF model is generated. Then, we find the similarity of each user query with the matrix after query pre-processing. For the proposed approach, we remove stop-words and apply lemmatization and POS tagging techniques for pre-processing. The proposed framework is implemented using the standard datasets used in the existing studies. The empirical analysis-based results show that the systems adopting the proposed technique takes less than five seconds to respond to user queries with maximum similarity. The proposed framework attains up to 84% accuracy.
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