Abstract
When ranking texts retrieved for a query, semantics of each term t in the texts is a fundamental basis. The semantics often depends on locality context (neighboring) terms of t in the texts. In this paper, we present a technique CTFA4TR that improves text rankers by encoding the term locality contexts to the assessment of term frequency (TF) of each term in the texts. Results of the TF assessment may be directly used to improve various kinds of text rankers, without calling for any revisions to algorithms and development processes of the rankers. Moreover, CTFA4TR is efficient to conduct the TF assessment online, and neither training process nor training data is required. Empirical evaluation shows that CTFA4TR significantly improves various kinds of text rankers. The contributions are of practical significance, since many text rankers were developed, and if they consider TF in ranking, CTFA4TR may be used to enhance their performance, without incurring any cost to them.
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Liu, RL., Lin, ZX. (2009). Improving Text Rankers by Term Locality Contexts. In: Lee, G.G., et al. Information Retrieval Technology. AIRS 2009. Lecture Notes in Computer Science, vol 5839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04769-5_24
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DOI: https://doi.org/10.1007/978-3-642-04769-5_24
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