Abstract
The use of phrases as part of similarity computations can enhance search effectiveness. But the gain comes at a cost, either in terms of index size, if all word-tuples are treated as queryable objects; or in terms of processing time, if postings lists for phrases are constructed at query time. There is also a lack of clarity as to which phrases are “interesting”, in the sense of capturing useful information. Here we explore several techniques for recognizing phrases using statistics of large-scale collections, and evaluate their quality.
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Gog, S., Moffat, A., Petri, M. (2015). On Identifying Phrases Using Collection Statistics. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_30
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DOI: https://doi.org/10.1007/978-3-319-16354-3_30
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