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The Use of Topic Representative Words in Text Categorization
Proceedings of the Australasian Document Computing Symposium (ADCS), стр. 7 pages. (2009)
Аннотация
We present a novel way to identify the representative words that are able to capture the topic of documents for use in text categorization. Our intuition is that not all word n-grams equally represent the topic of a document, and thus using all of them can potentially dilute the feature space. Hence, our aim is to investigate methods for identifying good indexing words, and empirically evaluate their impact on text categorization. To this end, we experiment with five different word sub-spaces: title words, first sentence words, keyphrases, domain-specific words, and named entities. We also test TF ? IDF-based unsupervised methods for extracting keyphrases and domain-specific words , and empirically verify their feasibility for text categorization. We demonstrate that using representative words outperforms a simple 1-gram model.
