Matches in SemOpenAlex for { <https://semopenalex.org/work/W2116954814> ?p ?o ?g. }
Showing items 1 to 83 of
83
with 100 items per page.
- W2116954814 abstract "Specialists who analyze online news have a hard time separating the wheat from the chaff. Moreover, automatic data-mining techniques like clustering of news streams into topical groups can fully recover the underlying true class labels of data if and only if all classes are well separated. In reality, especially for news streams, this is clearly not the case. The question to ask is thus this: if we cannot recover the full C classes by clustering, what is the largest K < C clusters we can find that best resemble the K underlying classes? Using the intuition that bursty topics are more likely to correspond to important events that are of interest to analysts, we propose several new bursty vector space models (B-VSM)for representing a news document. B-VSM takes into account the burstiness (across the full corpus and whole duration) of each constituent word in a document at the time of publication. We benchmarked our B-VSM against the classical TFIDF-VSM on the task of clustering a collection of news stream articles with known topic labels. Experimental results show that B-VSM was able to find the burstiest clusters/topics. Further, it also significantly improved the recall and precision for the top K clusters/topics." @default.
- W2116954814 created "2016-06-24" @default.
- W2116954814 creator A5010584734 @default.
- W2116954814 creator A5019546816 @default.
- W2116954814 creator A5090007199 @default.
- W2116954814 date "2007-10-01" @default.
- W2116954814 modified "2023-09-23" @default.
- W2116954814 title "Using Burstiness to Improve Clustering of Topics in News Streams" @default.
- W2116954814 cites W1505790831 @default.
- W2116954814 cites W1790954942 @default.
- W2116954814 cites W1972833205 @default.
- W2116954814 cites W1978394996 @default.
- W2116954814 cites W1998224037 @default.
- W2116954814 cites W2007584292 @default.
- W2116954814 cites W2007760849 @default.
- W2116954814 cites W2013927061 @default.
- W2116954814 cites W2026302857 @default.
- W2116954814 cites W2047298125 @default.
- W2116954814 cites W2055294489 @default.
- W2116954814 cites W2066680326 @default.
- W2116954814 cites W2092957858 @default.
- W2116954814 cites W2169279737 @default.
- W2116954814 cites W2918757710 @default.
- W2116954814 cites W4234917632 @default.
- W2116954814 cites W4236369288 @default.
- W2116954814 doi "https://doi.org/10.1109/icdm.2007.17" @default.
- W2116954814 hasPublicationYear "2007" @default.
- W2116954814 type Work @default.
- W2116954814 sameAs 2116954814 @default.
- W2116954814 citedByCount "45" @default.
- W2116954814 countsByYear W21169548142012 @default.
- W2116954814 countsByYear W21169548142013 @default.
- W2116954814 countsByYear W21169548142014 @default.
- W2116954814 countsByYear W21169548142015 @default.
- W2116954814 countsByYear W21169548142016 @default.
- W2116954814 countsByYear W21169548142017 @default.
- W2116954814 countsByYear W21169548142019 @default.
- W2116954814 countsByYear W21169548142020 @default.
- W2116954814 countsByYear W21169548142021 @default.
- W2116954814 countsByYear W21169548142022 @default.
- W2116954814 crossrefType "proceedings-article" @default.
- W2116954814 hasAuthorship W2116954814A5010584734 @default.
- W2116954814 hasAuthorship W2116954814A5019546816 @default.
- W2116954814 hasAuthorship W2116954814A5090007199 @default.
- W2116954814 hasConcept C111472728 @default.
- W2116954814 hasConcept C124101348 @default.
- W2116954814 hasConcept C132010649 @default.
- W2116954814 hasConcept C138885662 @default.
- W2116954814 hasConcept C154945302 @default.
- W2116954814 hasConcept C158379750 @default.
- W2116954814 hasConcept C23123220 @default.
- W2116954814 hasConcept C2781023610 @default.
- W2116954814 hasConcept C31258907 @default.
- W2116954814 hasConcept C41008148 @default.
- W2116954814 hasConcept C73555534 @default.
- W2116954814 hasConceptScore W2116954814C111472728 @default.
- W2116954814 hasConceptScore W2116954814C124101348 @default.
- W2116954814 hasConceptScore W2116954814C132010649 @default.
- W2116954814 hasConceptScore W2116954814C138885662 @default.
- W2116954814 hasConceptScore W2116954814C154945302 @default.
- W2116954814 hasConceptScore W2116954814C158379750 @default.
- W2116954814 hasConceptScore W2116954814C23123220 @default.
- W2116954814 hasConceptScore W2116954814C2781023610 @default.
- W2116954814 hasConceptScore W2116954814C31258907 @default.
- W2116954814 hasConceptScore W2116954814C41008148 @default.
- W2116954814 hasConceptScore W2116954814C73555534 @default.
- W2116954814 hasLocation W21169548141 @default.
- W2116954814 hasOpenAccess W2116954814 @default.
- W2116954814 hasPrimaryLocation W21169548141 @default.
- W2116954814 hasRelatedWork W1487010709 @default.
- W2116954814 hasRelatedWork W1983932473 @default.
- W2116954814 hasRelatedWork W1985477471 @default.
- W2116954814 hasRelatedWork W1999627569 @default.
- W2116954814 hasRelatedWork W2376314740 @default.
- W2116954814 hasRelatedWork W2376852021 @default.
- W2116954814 hasRelatedWork W2384888906 @default.
- W2116954814 hasRelatedWork W2387001852 @default.
- W2116954814 hasRelatedWork W2901160295 @default.
- W2116954814 hasRelatedWork W2029602998 @default.
- W2116954814 isParatext "false" @default.
- W2116954814 isRetracted "false" @default.
- W2116954814 magId "2116954814" @default.
- W2116954814 workType "article" @default.