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General Information
    • ISSN: 2382-6185
    • Abbreviated Title: Int. J. Knowl. Eng.
    • Frequency: Semiyearly
    • DOI: 10.18178/IJKE
    • Editor-in-Chief: Prof. Chen-Huei Chou
    • Executive Editor: Ms. Nina Lee
    • Indexed by: Google Scholar, Crossref, ProQuest
    • E-mail: ijke@ejournal.net
Prof. Chen-Huei Chou
College of Charleston, SC, USA
It is my honor to be the editor-in-chief of IJKE. I will do my best to help develop this journal better.
IJKE 2017 Vol.3(2): 80-85 ISSN: 2382-6185
doi: 10.18178/ijke.2017.3.2.091

Re-examining Google Tri-grams Measure (GTM) Sentence Similarity

Wong Lin Juan Linda, Chih How Bong, and Nung Kion Lee
Abstract—This paper examines text similarity approach based on Google n-gram dataset. Google Tri-grams Measure (GTM) is an unsupervised text similarity measure. The paper investigates the sentence similarity of GTM which in turn reveals the approach’s pitfalls. We also compared GTM’s sentence similarity measures on Li-30 sentence pairs, Microsoft Research Paraphrase Corpus paraphrase, Kaggle Quora Question Pairs competition’s dataset respectively against human judgement. Other sentence similarity measures are compared against GTM. We discovered GTM sentence similarity has a lot of weight on overlapped words count. However, despite the weakness, it still outperformed other replicated sentence similarity measures.

Index Terms—Google trigrams, pitfalls, sentence similarity, text similarity, trigrams, unsupervised, word similarity.

The authors are with Faculty Computer Science and Information Technology, Universiti Malaysia Sarawak, Malaysia (e-mail: 15020282@siswa.unimas.my, chbong@unimas.my, nklee@unimas.my).


Cite: Wong Lin Juan Linda, Chih How Bong, and Nung Kion Lee, "Re-examining Google Tri-grams Measure (GTM) Sentence Similarity," International Journal of Knowledge Engineering vol. 3, no. 2, pp. 80-85, 2017.

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