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Multiple-instance learning for text categorization based on semantic representation


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  • Text categorization is the fundamental bricks of other related researches in NLP. Up to now, researchers have proposed many effective text categorization methods and gained well performance. However, these methods are generally based on the raw features or low level features, e.g., tf or tfidf, while neglecting the semantic structures between words. Complex semantic information can influence the precision of text categorization. In this paper, we propose a new method to handle the semantic correlations between different words and text features from the representations and the learning schemes. We represent the document as multiple instances based on word2vec. Experiments validate the effectiveness of proposed method compared with those state-of-the-art text categorization methods.

    Mathematics Subject Classification: 97R40.


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  • Figure 1.  The structure of Bag-of-Words and Skip-Gram

    Figure 2.  Pseudo-code for mi-SVM

    Table 1.  Results of experiments on sougouC

    Model car finance IT health sport
    SVM + TF-IDF 0.8473 0.8420 0.8363 0.8326 0.8737
    SVM + Word2vec 0.9303 0.8571 0.8755 0.9163 0.9828
    mi-SVM + Word2vec 0.9599 0.8904 0.8943 0.9325 0.9842
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    Table 2.  Results of experiments on 20newsgroup

    Model SVM+tf-idf SVM+Word2vec mi-SVM+Word2vec
    Average 0.8508 0.8421 0.8619
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    DownLoad: CSV
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