Graph-based Representation for Sentence Similarity Measure : A Comparative Analysis

  • Authors

    • Siti Sakira Kamaruddin
    • Yuhanis Yusof
    • Nur Azzah Abu Bakar
    • Mohamed Ahmed Tayie
    • Ghaith Abdulsattar A.Jabbar Alkubaisi
    2018-04-06
    https://doi.org/10.14419/ijet.v7i2.14.11149
  • Graph Based Representation, Latent Semantic Analysis, Text Representation, Text Similarity Measure, TF-IDF.

  • Abstract

    Textual data are a rich source of knowledge; hence, sentence comparison has become one of the important tasks in text mining related works. Most previous work in text comparison are performed at document level, research suggest that comparing sentence level text is a non-trivial problem.  One of the reason is two sentences can convey the same meaning with totally dissimilar words.  This paper presents the results of a comparative analysis on three representation schemes i.e. term frequency inverse document frequency, Latent Semantic Analysis and Graph based representation using three similarity measures i.e. Cosine, Dice coefficient and Jaccard similarity to compare the similarity of sentences.  Results reveal that the graph based representation and the Jaccard similarity measure outperforms the others in terms of precision, recall and F-measures.

     

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  • How to Cite

    Sakira Kamaruddin, S., Yusof, Y., Azzah Abu Bakar, N., Ahmed Tayie, M., & Abdulsattar A.Jabbar Alkubaisi, G. (2018). Graph-based Representation for Sentence Similarity Measure : A Comparative Analysis. International Journal of Engineering & Technology, 7(2.14), 32-35. https://doi.org/10.14419/ijet.v7i2.14.11149

    Received date: 2018-04-06

    Accepted date: 2018-04-06

    Published date: 2018-04-06