e-space
Manchester Metropolitan University's Research Repository

    Sentence similarity based on semantic nets and corpus statistics

    Li, Yuhua, McLean, David A., Bandar, Zuhair A., O'Shea, James and Crockett, Keeley (2006) Sentence similarity based on semantic nets and corpus statistics. IEEE transactions on knowledge and data engineering, 18 (8). pp. 1138-1150. ISSN 1558-2191

    [img]
    Preview

    Download (384kB) | Preview

    Abstract

    Sentence similarity measures play an increasingly important role in text-related research and applications in areas such as text mining, Web page retrieval, and dialogue systems. Existing methods for computing sentence similarity have been adopted from approaches used for long text documents. These methods process sentences in a very high-dimensional space and are consequently inefficient, require human input, and are not adaptable to some application domains. This paper focuses directly on computing the similarity between very short texts of sentence length. It presents an algorithm that takes account of semantic information and word order information implied in the sentences. The semantic similarity of two sentences is calculated using information from a structured lexical database and from corpus statistics. The use of a lexical database enables our method to model human common sense knowledge and the incorporation of corpus statistics allows our method to be adaptable to different domains. The proposed method can be used in a variety of applications that involve text knowledge representation and discovery. Experiments on two sets of selected sentence pairs demonstrate that the proposed method provides a similarity measure that shows a significant correlation to human intuition.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    2,773Downloads
    6 month trend
    495Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record