Leveraging linguistic resources for improving neural text classification

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

    Abstract

    This paper presents a deep linguistic attentional framework which incorporates word level concept information into neural classification models. While learning neural classification models often requires a large amount of labelled data, linguistic concept information can be obtained from external knowledge, such as pre-trained word embeddings, WordNet for common text and MetaMap for biomedical text. We explore two different ways of incorporating word level concept annotations, and show that leveraging concept annotation scan boost the model performance and reduce the need for large amounts of labelled data. Experiments on various data sets validate the effectiveness of the proposed method.
    Original languageEnglish
    Title of host publicationAustralasian Language Technology Association Workshop, ALTA 2017
    Subtitle of host publication6–8 December 2017, Brisbane, Australia, Proceedings
    EditorsJojo Sze-Meng Wong, Gholamreza Haffari
    Place of PublicationMelbourne Victoria Australia
    PublisherAustralian Language Technology Association (ALTA)
    Pages34-42
    Number of pages9
    Volume15
    Edition2017
    ISBN (Print)1834-7037
    Publication statusPublished - 2017
    EventAustralasian Language Technology Association Workshop 2017 - Queensland University of Technology, Brisbane, Australia
    Duration: 6 Dec 20178 Dec 2017
    Conference number: 15th

    Conference

    ConferenceAustralasian Language Technology Association Workshop 2017
    Abbreviated titleALTA Workshop 2017
    CountryAustralia
    CityBrisbane
    Period6/12/178/12/17
    OtherIn 2017, the Australasian Language Technology Association Workshop (ALTA) will be held at the Queensland University of Technology in Brisbane.

    The workshop is the key forum in Australia and New Zealand for sharing research results in natural language processing and computational linguistics, with featured keynote speakers, as well as presentations and posters from student, industry, and early-career researchers.

    ALTA 2017 is co-located with ADCS 2017.

    Cite this

    Liu, M., Haffari, G., Buntine, W., & Ananda-Rajah, M. (2017). Leveraging linguistic resources for improving neural text classification. In J. S-M. Wong, & G. Haffari (Eds.), Australasian Language Technology Association Workshop, ALTA 2017: 6–8 December 2017, Brisbane, Australia, Proceedings (2017 ed., Vol. 15, pp. 34-42). Melbourne Victoria Australia: Australian Language Technology Association (ALTA).
    Liu, Ming ; Haffari, Gholamreza ; Buntine, Wray ; Ananda-Rajah, Michelle. / Leveraging linguistic resources for improving neural text classification. Australasian Language Technology Association Workshop, ALTA 2017: 6–8 December 2017, Brisbane, Australia, Proceedings . editor / Jojo Sze-Meng Wong ; Gholamreza Haffari. Vol. 15 2017. ed. Melbourne Victoria Australia : Australian Language Technology Association (ALTA), 2017. pp. 34-42
    @inproceedings{3a8ca734c3484736ae94cd250d70a557,
    title = "Leveraging linguistic resources for improving neural text classification",
    abstract = "This paper presents a deep linguistic attentional framework which incorporates word level concept information into neural classification models. While learning neural classification models often requires a large amount of labelled data, linguistic concept information can be obtained from external knowledge, such as pre-trained word embeddings, WordNet for common text and MetaMap for biomedical text. We explore two different ways of incorporating word level concept annotations, and show that leveraging concept annotation scan boost the model performance and reduce the need for large amounts of labelled data. Experiments on various data sets validate the effectiveness of the proposed method.",
    author = "Ming Liu and Gholamreza Haffari and Wray Buntine and Michelle Ananda-Rajah",
    year = "2017",
    language = "English",
    isbn = "1834-7037",
    volume = "15",
    pages = "34--42",
    editor = "Wong, {Jojo Sze-Meng} and Gholamreza Haffari",
    booktitle = "Australasian Language Technology Association Workshop, ALTA 2017",
    publisher = "Australian Language Technology Association (ALTA)",
    edition = "2017",

    }

    Liu, M, Haffari, G, Buntine, W & Ananda-Rajah, M 2017, Leveraging linguistic resources for improving neural text classification. in JS-M Wong & G Haffari (eds), Australasian Language Technology Association Workshop, ALTA 2017: 6–8 December 2017, Brisbane, Australia, Proceedings . 2017 edn, vol. 15, Australian Language Technology Association (ALTA), Melbourne Victoria Australia, pp. 34-42, Australasian Language Technology Association Workshop 2017, Brisbane, Australia, 6/12/17.

    Leveraging linguistic resources for improving neural text classification. / Liu, Ming; Haffari, Gholamreza; Buntine, Wray; Ananda-Rajah, Michelle.

    Australasian Language Technology Association Workshop, ALTA 2017: 6–8 December 2017, Brisbane, Australia, Proceedings . ed. / Jojo Sze-Meng Wong; Gholamreza Haffari. Vol. 15 2017. ed. Melbourne Victoria Australia : Australian Language Technology Association (ALTA), 2017. p. 34-42.

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

    TY - GEN

    T1 - Leveraging linguistic resources for improving neural text classification

    AU - Liu, Ming

    AU - Haffari, Gholamreza

    AU - Buntine, Wray

    AU - Ananda-Rajah, Michelle

    PY - 2017

    Y1 - 2017

    N2 - This paper presents a deep linguistic attentional framework which incorporates word level concept information into neural classification models. While learning neural classification models often requires a large amount of labelled data, linguistic concept information can be obtained from external knowledge, such as pre-trained word embeddings, WordNet for common text and MetaMap for biomedical text. We explore two different ways of incorporating word level concept annotations, and show that leveraging concept annotation scan boost the model performance and reduce the need for large amounts of labelled data. Experiments on various data sets validate the effectiveness of the proposed method.

    AB - This paper presents a deep linguistic attentional framework which incorporates word level concept information into neural classification models. While learning neural classification models often requires a large amount of labelled data, linguistic concept information can be obtained from external knowledge, such as pre-trained word embeddings, WordNet for common text and MetaMap for biomedical text. We explore two different ways of incorporating word level concept annotations, and show that leveraging concept annotation scan boost the model performance and reduce the need for large amounts of labelled data. Experiments on various data sets validate the effectiveness of the proposed method.

    M3 - Conference Paper

    SN - 1834-7037

    VL - 15

    SP - 34

    EP - 42

    BT - Australasian Language Technology Association Workshop, ALTA 2017

    A2 - Wong, Jojo Sze-Meng

    A2 - Haffari, Gholamreza

    PB - Australian Language Technology Association (ALTA)

    CY - Melbourne Victoria Australia

    ER -

    Liu M, Haffari G, Buntine W, Ananda-Rajah M. Leveraging linguistic resources for improving neural text classification. In Wong JS-M, Haffari G, editors, Australasian Language Technology Association Workshop, ALTA 2017: 6–8 December 2017, Brisbane, Australia, Proceedings . 2017 ed. Vol. 15. Melbourne Victoria Australia: Australian Language Technology Association (ALTA). 2017. p. 34-42