Matches in SemOpenAlex for { <https://semopenalex.org/work/W4311141290> ?p ?o ?g. }
- W4311141290 endingPage "103215" @default.
- W4311141290 startingPage "103215" @default.
- W4311141290 abstract "Traditional topic models are based on the bag-of-words assumption, which states that the topic assignment of each word is independent of the others. However, this assumption ignores the relationship between words, which may hinder the quality of extracted topics. To address this issue, some recent works formulate documents as graphs based on word co-occurrence patterns. It assumes that if two words co-occur frequently, they should have the same topic. Nevertheless, it introduces noise edges into the model and thus hinders topic quality since two words co-occur frequently do not mean that they are on the same topic. In this paper, we use the commonsense relationship between words as a bridge to connect the words in each document. Compared to word co-occurrence, the commonsense relationship can explicitly imply the semantic relevance between words, which can be utilized to filter out noise edges. We use a relational graph neural network to capture the relation information in the graph. Moreover, manifold regularization is utilized to constrain the documents’ topic distributions. Experimental results on a public dataset show that our method is effective at extracting topics compared to baseline methods." @default.
- W4311141290 created "2022-12-23" @default.
- W4311141290 creator A5043915187 @default.
- W4311141290 creator A5058344611 @default.
- W4311141290 creator A5089123257 @default.
- W4311141290 date "2023-03-01" @default.
- W4311141290 modified "2023-10-13" @default.
- W4311141290 title "Graph neural topic model with commonsense knowledge" @default.
- W4311141290 cites W1977830790 @default.
- W4311141290 cites W2001082470 @default.
- W4311141290 cites W2038043464 @default.
- W4311141290 cites W2048195127 @default.
- W4311141290 cites W2061922307 @default.
- W4311141290 cites W2086038979 @default.
- W4311141290 cites W2097089247 @default.
- W4311141290 cites W2104210067 @default.
- W4311141290 cites W2112104211 @default.
- W4311141290 cites W2150461699 @default.
- W4311141290 cites W2168084958 @default.
- W4311141290 cites W2169606435 @default.
- W4311141290 cites W2174706414 @default.
- W4311141290 cites W2250539671 @default.
- W4311141290 cites W2251734881 @default.
- W4311141290 cites W2561529111 @default.
- W4311141290 cites W2604314403 @default.
- W4311141290 cites W2741681315 @default.
- W4311141290 cites W2892358182 @default.
- W4311141290 cites W2897454190 @default.
- W4311141290 cites W2904432433 @default.
- W4311141290 cites W2952576857 @default.
- W4311141290 cites W2963959132 @default.
- W4311141290 cites W3035007341 @default.
- W4311141290 cites W3035650037 @default.
- W4311141290 cites W3045464143 @default.
- W4311141290 cites W3103615620 @default.
- W4311141290 cites W3112012747 @default.
- W4311141290 cites W3158616546 @default.
- W4311141290 cites W3165405064 @default.
- W4311141290 cites W3174820758 @default.
- W4311141290 cites W3207972072 @default.
- W4311141290 cites W3213117297 @default.
- W4311141290 cites W4210327919 @default.
- W4311141290 cites W4212774083 @default.
- W4311141290 cites W4233135949 @default.
- W4311141290 cites W4283815582 @default.
- W4311141290 cites W91766825 @default.
- W4311141290 doi "https://doi.org/10.1016/j.ipm.2022.103215" @default.
- W4311141290 hasPublicationYear "2023" @default.
- W4311141290 type Work @default.
- W4311141290 citedByCount "1" @default.
- W4311141290 countsByYear W43111412902023 @default.
- W4311141290 crossrefType "journal-article" @default.
- W4311141290 hasAuthorship W4311141290A5043915187 @default.
- W4311141290 hasAuthorship W4311141290A5058344611 @default.
- W4311141290 hasAuthorship W4311141290A5089123257 @default.
- W4311141290 hasConcept C106131492 @default.
- W4311141290 hasConcept C120567893 @default.
- W4311141290 hasConcept C132525143 @default.
- W4311141290 hasConcept C154945302 @default.
- W4311141290 hasConcept C158154518 @default.
- W4311141290 hasConcept C17744445 @default.
- W4311141290 hasConcept C199539241 @default.
- W4311141290 hasConcept C204321447 @default.
- W4311141290 hasConcept C23123220 @default.
- W4311141290 hasConcept C2524010 @default.
- W4311141290 hasConcept C30542707 @default.
- W4311141290 hasConcept C31972630 @default.
- W4311141290 hasConcept C33923547 @default.
- W4311141290 hasConcept C41008148 @default.
- W4311141290 hasConcept C50644808 @default.
- W4311141290 hasConcept C80444323 @default.
- W4311141290 hasConcept C90805587 @default.
- W4311141290 hasConceptScore W4311141290C106131492 @default.
- W4311141290 hasConceptScore W4311141290C120567893 @default.
- W4311141290 hasConceptScore W4311141290C132525143 @default.
- W4311141290 hasConceptScore W4311141290C154945302 @default.
- W4311141290 hasConceptScore W4311141290C158154518 @default.
- W4311141290 hasConceptScore W4311141290C17744445 @default.
- W4311141290 hasConceptScore W4311141290C199539241 @default.
- W4311141290 hasConceptScore W4311141290C204321447 @default.
- W4311141290 hasConceptScore W4311141290C23123220 @default.
- W4311141290 hasConceptScore W4311141290C2524010 @default.
- W4311141290 hasConceptScore W4311141290C30542707 @default.
- W4311141290 hasConceptScore W4311141290C31972630 @default.
- W4311141290 hasConceptScore W4311141290C33923547 @default.
- W4311141290 hasConceptScore W4311141290C41008148 @default.
- W4311141290 hasConceptScore W4311141290C50644808 @default.
- W4311141290 hasConceptScore W4311141290C80444323 @default.
- W4311141290 hasConceptScore W4311141290C90805587 @default.
- W4311141290 hasIssue "2" @default.
- W4311141290 hasLocation W43111412901 @default.
- W4311141290 hasOpenAccess W4311141290 @default.
- W4311141290 hasPrimaryLocation W43111412901 @default.
- W4311141290 hasRelatedWork W1835907303 @default.
- W4311141290 hasRelatedWork W2085384747 @default.
- W4311141290 hasRelatedWork W2088166309 @default.
- W4311141290 hasRelatedWork W2106071040 @default.
- W4311141290 hasRelatedWork W2276587472 @default.