A New Language Model Combining Single and Compound Terms

Arezki Hammache, R. Ahmed-Ouamer, M. Boughanem
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引用次数: 1

Abstract

Most traditional information retrieval systems are based on single terms indexing. However, it is admitted that semantic content of a document (or a query) cannot be accurately captured by a simple set of independent keywords. Although, several works have incorporated phrases or other syntactic information in IR, such attempts have shown slight benefit, at best. Particularly in language modeling approaches this is achieved through the use of the big ram or n-gram models. However, in these models all big rams/n-grams are considered and weighted uniformly. In this paper we introduce a new approach to weight and consider only certain types of N-grams "compound terms". Experimental results on three test collections showed an improvement.
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单词与复合词结合的新语言模型
大多数传统的信息检索系统是基于单词索引的。然而,人们承认,文档(或查询)的语义内容不能通过一组简单的独立关键字来准确捕获。尽管有几部作品在IR中加入了短语或其他语法信息,但这种尝试充其量只能显示出轻微的好处。特别是在语言建模方法中,这是通过使用大内存或n-gram模型来实现的。然而,在这些模型中,所有的大公羊/n-克被统一考虑和加权。在本文中,我们引入了一种新的权值计算方法,并且只考虑特定类型的n -g“复合项”。在三个测试集上的实验结果表明了改进的效果。
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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