NDLI: The refined MI: A significant improvement to mutual information

Content Provider	IEEE Xplore Digital Library
Author	Alrabiah, M. Al-Salman, A. Atwell, E.
Copyright Year	2014
Description	Author affiliation: Fac. of Eng., Univ. of Leeds, Leeds, UK (Atwell, E.) \|\| Comput. Sci. Dept., King Saud Univ., Riyadh, Saudi Arabia (Alrabiah, M.; Al-Salman, A.)
Abstract	Distributional lexical semantics is an empirical approach that is mainly concerned with modeling words' meanings using word distribution statistics gathered from very large corpora. It is basically built on the Distributional Hypothesis by Zellig Harris in 1970, which states that the difference in words' meanings is associated with the difference in their distribution in text. This difference in meaning originates from two kinds of relations between words, which are syntagmatic and paradigmatic relations. Syntagmatic relations are linear combinatorial relations that are established between words that co-occur together in sequential text; while paradigmatic relations are substitutional relations that are established between words that occur in the same context, share neighboring words, but do not co-occur in the same text. In this paper, we present a new association measure, the Refined MI, for measuring syntagmatic relations between words. In addition, an experimental study to evaluate the performance of the proposed measure is presented. The measure showed outstanding results in identifying significant co-occurrences from Classical Arabic text.
Starting Page	132
Ending Page	135
File Size	193468
Page Count	4
File Format	PDF
ISBN	9781479953301
DOI	10.1109/IALP.2014.6973512
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-20
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Semantics Classical Arabic Mutual Information Educational institutions Frequency conversion Frequency measurement Distributional semantics Mutual information Association measures Pragmatics
Content Type	Text
Resource Type	Article

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