A new term ranking method based on relation extraction and graph model for text classification

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    2 Citations (Scopus)
    2 Downloads (Pure)

    Abstract

    Term frequency and document frequency are currently used to measure term significance in text classification. However, these measures cannot provide sufficient information to differentiate important terms. Thus, in this research, a new term ranking (weighting) approach for text classification will be proposed.
    The approach firstly is based on relations among terms to estimates the important levels of terms in a document. Secondly, the proposed approach provides a considerable representation for the text documents. The results from experiment show that with the same data in Wikipedia corpus the term weighting approach provides higher accuracy in comparison to the popular approaches based on term frequency.
    Original languageEnglish
    Title of host publicationASCS '11 Proceedings of Thirty-Fourth Australasian Computer Science Conference
    EditorsMark Reynold
    Place of PublicationDarlinghurst, Australia
    PublisherAustralian Computer Society
    Pages145-152
    Number of pages7
    Volume113
    ISBN (Print)9781920682934
    Publication statusPublished - 17 Jan 2011
    Event34th Australasian Computer Science Conference (ACSC 2011) - Perth, Perth, Australia
    Duration: 17 Jan 201120 Jan 2011
    https://50years.acs.org.au/content/dam/acs/50-years/journals/crpit/Vol113.pdf

    Conference

    Conference34th Australasian Computer Science Conference (ACSC 2011)
    Abbreviated titleACSC 2011
    CountryAustralia
    CityPerth
    Period17/01/1120/01/11
    Internet address

    Fingerprint

    Experiments

    Cite this

    Hyunh, D., Tran, D., Ma, W., & Sharma, D. (2011). A new term ranking method based on relation extraction and graph model for text classification. In M. Reynold (Ed.), ASCS '11 Proceedings of Thirty-Fourth Australasian Computer Science Conference (Vol. 113, pp. 145-152). Darlinghurst, Australia: Australian Computer Society.
    Hyunh, Dat ; Tran, Dat ; Ma, Wanli ; Sharma, Dharmendra. / A new term ranking method based on relation extraction and graph model for text classification. ASCS '11 Proceedings of Thirty-Fourth Australasian Computer Science Conference. editor / Mark Reynold. Vol. 113 Darlinghurst, Australia : Australian Computer Society, 2011. pp. 145-152
    @inproceedings{847be24086644f68b4f25d206225bb72,
    title = "A new term ranking method based on relation extraction and graph model for text classification",
    abstract = "Term frequency and document frequency are currently used to measure term significance in text classification. However, these measures cannot provide sufficient information to differentiate important terms. Thus, in this research, a new term ranking (weighting) approach for text classification will be proposed.The approach firstly is based on relations among terms to estimates the important levels of terms in a document. Secondly, the proposed approach provides a considerable representation for the text documents. The results from experiment show that with the same data in Wikipedia corpus the term weighting approach provides higher accuracy in comparison to the popular approaches based on term frequency.",
    keywords = "Machine Learning, Text Classification",
    author = "Dat Hyunh and Dat Tran and Wanli Ma and Dharmendra Sharma",
    year = "2011",
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    language = "English",
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    Hyunh, D, Tran, D, Ma, W & Sharma, D 2011, A new term ranking method based on relation extraction and graph model for text classification. in M Reynold (ed.), ASCS '11 Proceedings of Thirty-Fourth Australasian Computer Science Conference. vol. 113, Australian Computer Society, Darlinghurst, Australia, pp. 145-152, 34th Australasian Computer Science Conference (ACSC 2011), Perth, Australia, 17/01/11.

    A new term ranking method based on relation extraction and graph model for text classification. / Hyunh, Dat; Tran, Dat; Ma, Wanli; Sharma, Dharmendra.

    ASCS '11 Proceedings of Thirty-Fourth Australasian Computer Science Conference. ed. / Mark Reynold. Vol. 113 Darlinghurst, Australia : Australian Computer Society, 2011. p. 145-152.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

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    AU - Ma, Wanli

    AU - Sharma, Dharmendra

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    N2 - Term frequency and document frequency are currently used to measure term significance in text classification. However, these measures cannot provide sufficient information to differentiate important terms. Thus, in this research, a new term ranking (weighting) approach for text classification will be proposed.The approach firstly is based on relations among terms to estimates the important levels of terms in a document. Secondly, the proposed approach provides a considerable representation for the text documents. The results from experiment show that with the same data in Wikipedia corpus the term weighting approach provides higher accuracy in comparison to the popular approaches based on term frequency.

    AB - Term frequency and document frequency are currently used to measure term significance in text classification. However, these measures cannot provide sufficient information to differentiate important terms. Thus, in this research, a new term ranking (weighting) approach for text classification will be proposed.The approach firstly is based on relations among terms to estimates the important levels of terms in a document. Secondly, the proposed approach provides a considerable representation for the text documents. The results from experiment show that with the same data in Wikipedia corpus the term weighting approach provides higher accuracy in comparison to the popular approaches based on term frequency.

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    EP - 152

    BT - ASCS '11 Proceedings of Thirty-Fourth Australasian Computer Science Conference

    A2 - Reynold, Mark

    PB - Australian Computer Society

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    Hyunh D, Tran D, Ma W, Sharma D. A new term ranking method based on relation extraction and graph model for text classification. In Reynold M, editor, ASCS '11 Proceedings of Thirty-Fourth Australasian Computer Science Conference. Vol. 113. Darlinghurst, Australia: Australian Computer Society. 2011. p. 145-152