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Sentence similarity based on semantic nets and corpus statistics

Li, Yuhua, McLean, David, Bandar, Zuhair, O’Shea, James D. 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. [Journal article]

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URL: http://ieeexplore.ieee.org/iel5/69/34468/01644735.pdf?isnumber=34468&prod=JNL&arnumber=1644735&arSt=+1138&ared=+1150&arAuthor=Li%2C+Y.%3B+McLean%2C+D.%3B+Bandar%2C+Z.A.%3B+O%27Shea%2C+J.D.%3B+Crockett%2C+K.


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.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:8631
Deposited By: Dr Yuhua Li
Deposited On:26 Jan 2010 16:27
Last Modified:09 May 2016 10:55

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