Abstract
This paper focuses on subject shift in chronologically ordered news story streams, and presents a method for topic tracking which makes use of the subject-shift. For finding the discussion of a topic (we call it subject term), we applied keygraph method to each story. Similar to tf*idf method, keygraph is a term weighting method which is based on co-occurrence graphs consisting high frequency terms and their co-occurrence terms. Subject-shifts are identified based on the difference between two types of subject terms: one is extracted from a test story itself, and another is extracted from the test story by using topic terms (terms related to a topic) of initial positive training stories. The method was tested on the TDT English corpus, and the results showed that the system is competitive to other sites, even for a small number of initial positive training stories.
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Fukumoto, F., Suzuki, Y. (2009). Using Graph-Based Indexing to Identify Subject-Shift in Topic Tracking. In: Vetulani, Z., Uszkoreit, H. (eds) Human Language Technology. Challenges of the Information Society. LTC 2007. Lecture Notes in Computer Science(), vol 5603. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04235-5_34
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DOI: https://doi.org/10.1007/978-3-642-04235-5_34
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