Adaptive web mining of bilingual lexicons for cross language information retrieval

Lei Shi
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引用次数: 3

Abstract

Bilingual web pages contain abundant term translation knowledge which is crucial for query translation in Cross Language Information Retrieval systems. But it is a challenging task to extract term translations from bilingual web pages due to the variation in web page layouts and writing styles. In this paper, based on the observation that translation pairs on the same web page tend to appear following similar patterns, a new extraction model is proposed to adaptively learn extraction patterns and exploit them to facilitate term translation mining from bilingual web pages. Experiments reflect that this model can significantly improve extraction coverage while maintaining high accuracy. It improves query translation in cross-language information retrieval, leading to significantly higher retrieval effectiveness on TREC collections.
面向跨语言信息检索的双语词汇自适应web挖掘
双语网页包含着丰富的术语翻译知识,这对跨语言信息检索系统中的查询翻译至关重要。但是,由于网页布局和写作风格的差异,从双语网页中提取术语翻译是一项具有挑战性的任务。本文基于对同一网页上翻译对出现相似模式的观察,提出了一种新的抽取模型,自适应学习抽取模式,并利用抽取模式进行双语网页术语翻译挖掘。实验表明,该模型在保持较高的提取精度的同时,显著提高了提取覆盖率。它改进了跨语言信息检索中的查询翻译,显著提高了TREC集合的检索效率。
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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