A
Aminul Islam
Researcher at University of Louisiana at Lafayette
Publications - 46
Citations - 1303
Aminul Islam is an academic researcher from University of Louisiana at Lafayette. The author has contributed to research in topics: Semantic similarity & Longest common subsequence problem. The author has an hindex of 12, co-authored 43 publications receiving 1199 citations. Previous affiliations of Aminul Islam include Dalhousie University & University of Ottawa.
Papers
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Journal ArticleDOI
Semantic text similarity using corpus-based word similarity and string similarity
Aminul Islam,Diana Inkpen +1 more
TL;DR: A method for measuring the semantic similarity of texts using a corpus-based measure of semantic word similarity and a normalized and modified version of the Longest Common Subsequence string matching algorithm is presented.
Proceedings Article
Second Order Co-occurrence PMI for Determining the Semantic Similarity of Words
Aminul Islam,Diana Inkpen +1 more
TL;DR: A new corpus-based method, called Second Order Co-occurrencePMI (SOC-PMI), uses Pointwise Mutual Information to sort lists of important neighbor words of the two target words to calculate the relative semantic similarity.
Proceedings ArticleDOI
Real-Word Spelling Correction using Google Web 1T 3-grams
Aminul Islam,Diana Inkpen +1 more
TL;DR: A method for detecting and correcting multiple real-word spelling errors using the Google Web IT 3-gram data set and a normalized and modified version of the Longest Common Subsequence (LCS) string matching algorithm.
Semantic similarity of short texts
Aminul Islam,Diana Inkpen +1 more
TL;DR: This paper presents a method for measuring the semantic similarity of texts using a corpus based measure of semantic word similarity and a normalized and modified versions of the Longest Common Subsequence string matching algorithm.
Book ChapterDOI
Text similarity using google tri-grams
TL;DR: Experimental results on a standard data set show that the proposed unsupervised method outperforms the state-of-the-art supervised method and the improvement achieved is statistically significant at 0.05 level.