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Information Extraction System Based on Hidden Markov Model

机译:基于隐马尔可夫模型的信息抽取系统

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摘要

A novel approach using Hidden Markov Model (HMM) for the task of finding prices of products on internet sites is proposed in this paper. The proposed Information Extraction System based on HMM (IESHMM) utilizes HMM for its capability to process temporal information. The proposed IESHMM first processes web pages that are returned from search engines and then extracts specific fields such as prices, descriptions, locations, images of products, and other information of interest. The proposed IESHMM is evaluated with real-world problems and compared with a conventional method. The results show that the proposed IESHMM outperforms the other method by 22.9 % and 37.2% in terms of average recall and average precision, respectively.
机译:本文提出了一种使用隐马尔可夫模型(HMM)的新颖方法,用于在互联网上查找产品价格。所提出的基于HMM的信息提取系统(IESHMM)利用HMM的能力来处理时间信息。提议的IESHMM首先处理从搜索引擎返回的网页,然后提取特定字段,例如价格,描述,位置,产品图像以及其他感兴趣的信息。所提出的IESHMM在实际问题中进行了评估,并与传统方法进行了比较。结果表明,提出的IESHMM在平均召回率和平均精度方面分别比其他方法高22.9%和37.2%。

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