Abstract
A widely used approach for keyword extraction and content-based tag recommendation is ranking terms according to some statistical criteria. In many cases documents such as news articles and product reviews are in some specific domains. Domain knowledge may be important information for term rankings. In this paper, we present to model domain knowledge using latent topic models, referred to as Domain-Topic Model (DTM). Using DTM we perform domain-specific term rankings according to the relatedness between terms and domains. Experimental results on both keyword extraction and tag recommendation show advantages of DTM for domain-specific term rankings.
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Liu, Z., Sun, M. (2010). Domain-Specific Term Rankings Using Topic Models. In: Cheng, PJ., Kan, MY., Lam, W., Nakov, P. (eds) Information Retrieval Technology. AIRS 2010. Lecture Notes in Computer Science, vol 6458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17187-1_44
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DOI: https://doi.org/10.1007/978-3-642-17187-1_44
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