NDLI: Paraphrasing invariance coefficient: measuring para-query invariance of search engines

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Paraphrasing invariance coefficient: measuring para-query invariance of search engines

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Paraphrasing invariance coefficient: measuring para-query invariance of search engines

Content Provider	ACM Digital Library
Author	Yan, Jinyun Yu, Huiwen Fang, Yihan Imielinski, Tomasz Kelly, Peter Eldridge, Kurt
Abstract	Paraphrasing is the restatement (or reuse) of text which preserves its meaning in another form. A para-query is a para-phrase of a search query. Humans easily recognize para-queries, but search engines are still far away from it. We claim that in order for a search engine to be called semantic it is necessary that it recognizes para-queries by returning the same search results for all para-queries of a given query. Recognizing para-queries is an important and desired ability of a search engine. It can relieve users of the burden of rephrasing queries in order to improve the relevance of results. In this paper, we cover two main threads: monolingual para-query generation (PG) and para-query recognition measurement (PRM). Para-query generation aims to automatically generate as many English para-queries as possible for a given query. We propose a novel game "Rephraser" to tackle this problem. Hundreds of para-query templates are extracted from the game's output and used to compose tens of thousands of para-queries. The goal of para-query recognition measurement is to examine to what level search engines recognize para-queries. We propose the concept of paraphrasing invariance coefficient (PIC) which is defined as the probability that search results are the same for a pair of para-queries. By using para-queries generated from the game, we design experiments to measure search engines' PIC. Results show that today's leading search engines are still inferior to human ability in recognizing para-queries. It is a long way ahead for search to be truly semantic.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450301305
DOI	10.1145/1863879.1863880
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-04-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Para-query generation Semantic search engine Para-query recognition measurement Rephraser game Paraphrasing invariance coefficient
Content Type	Text
Resource Type	Article

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