Difference between revisions of "Exploiting Wikipedia for Directional Inferential Text Similarity"
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| + | | title = Exploiting Wikipedia for Directional Inferential Text Similarity | ||
| + | | date = 2008 | ||
| + | | authors = [[Leong Chee Wee]]<br />[[Samer Hassan]] | ||
| + | | doi = 10.1109/ITNG.2008.190 | ||
| + | | link = http://ieeexplore.ieee.org/iel5/4492437/4492438/04492561.pdf | ||
| + | }} | ||
'''Exploiting Wikipedia for Directional Inferential Text Similarity''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Leong Chee Wee]] and [[Samer Hassan]]. | '''Exploiting Wikipedia for Directional Inferential Text Similarity''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Leong Chee Wee]] and [[Samer Hassan]]. | ||
== Overview == | == Overview == | ||
In natural languages, variability of semantic expression refers to the situation where the same meaning can be inferred from different words or texts. Given that many [[natural language processing]] tasks nowadays (e.g. [[question answering]], [[information retrieval]], document summarization) often model this variability by requiring a specific target meaning to be inferred from different text variants, it is helpful to capture text similarity in a directional manner to serve such inference needs. In this paper, authors show how [[Wikipedia]] can be used as a semantic resource to build a directional inferential similarity metric between words, and subsequently, texts. Through experiments, authors show that Wikipedia-based metric performs significantly better when applied to a standard evaluation dataset, with a reduction in error rate of 16.1% over the random metric baseline. | In natural languages, variability of semantic expression refers to the situation where the same meaning can be inferred from different words or texts. Given that many [[natural language processing]] tasks nowadays (e.g. [[question answering]], [[information retrieval]], document summarization) often model this variability by requiring a specific target meaning to be inferred from different text variants, it is helpful to capture text similarity in a directional manner to serve such inference needs. In this paper, authors show how [[Wikipedia]] can be used as a semantic resource to build a directional inferential similarity metric between words, and subsequently, texts. Through experiments, authors show that Wikipedia-based metric performs significantly better when applied to a standard evaluation dataset, with a reduction in error rate of 16.1% over the random metric baseline. | ||
Revision as of 09:02, 10 October 2019
Authors | Leong Chee Wee Samer Hassan |
|---|---|
Publication date | 2008 |
DOI | 10.1109/ITNG.2008.190 |
Links | Original |
Exploiting Wikipedia for Directional Inferential Text Similarity - scientific work related to Wikipedia quality published in 2008, written by Leong Chee Wee and Samer Hassan.
Overview
In natural languages, variability of semantic expression refers to the situation where the same meaning can be inferred from different words or texts. Given that many natural language processing tasks nowadays (e.g. question answering, information retrieval, document summarization) often model this variability by requiring a specific target meaning to be inferred from different text variants, it is helpful to capture text similarity in a directional manner to serve such inference needs. In this paper, authors show how Wikipedia can be used as a semantic resource to build a directional inferential similarity metric between words, and subsequently, texts. Through experiments, authors show that Wikipedia-based metric performs significantly better when applied to a standard evaluation dataset, with a reduction in error rate of 16.1% over the random metric baseline.
