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- rswk_00%3a%22Google %2f suchmaschine %2f ranking %28BVB%29%22 2
- rswk_00%3a%22Google %2f suchmaschinen %2f ranking %28BVB%29%22 2
- rswk_00%3a%22Google %2f suchmascheine %2f ranking %28BVB%29%22 2
- rswk_00%3a%22Google %2f suchmachinen %2f ranking %28BVB%29%22 2
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Vechtomova, O.; Karamuftuoglum, M.; Robertson, S.E.: On document relevance and lexical cohesion between query terms (2006)
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- Abstract
- Lexical cohesion is a property of text, achieved through lexical-semantic relations between words in text. Most information retrieval systems make use of lexical relations in text only to a limited extent. In this paper we empirically investigate whether the degree of lexical cohesion between the contexts of query terms' occurrences in a document is related to its relevance to the query. Lexical cohesion between distinct query terms in a document is estimated on the basis of the lexical-semantic relations (repetition, synonymy, hyponymy and sibling) that exist between there collocates - words that co-occur with them in the same windows of text. Experiments suggest significant differences between the lexical cohesion in relevant and non-relevant document sets exist. A document ranking method based on lexical cohesion shows some performance improvements.
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Vechtomova, O.: ¬A method for automatic extraction of multiword units representing business aspects from user reviews (2014)
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- Abstract
- The article describes a semi-supervised approach to extracting multiword aspects of user-written reviews that belong to a given category. The method starts with a small set of seed words, representing the target category, and calculates distributional similarity between the candidate and seed words. We compare 3 distributional similarity measures (Lin's, Weeds's, and balAPinc), and a document retrieval function, BM25, adapted as a word similarity measure. We then introduce a method for identifying multiword aspects by using a combination of syntactic rules and a co-occurrence association measure. Finally, we describe a method for ranking multiword aspects by the likelihood of belonging to the target aspect category. The task used for evaluation is extraction of restaurant dish names from a corpus of restaurant reviews.