Please use this identifier to cite or link to this item:
|Title:||Commonsense-based topic modeling||Authors:||Rajagopal, D.
|Issue Date:||2013||Citation:||Rajagopal, D., Olsher, D., Cambria, E., Kwok, K. (2013). Commonsense-based topic modeling. Proceedings of the 2nd International Workshop on Issues of Sentiment Discovery and Opinion Mining, WISDOM 2013 - Held in Conjunction with SIGKDD 2013 : -. ScholarBank@NUS Repository. https://doi.org/10.1145/2502069.2502075||Abstract:||Topic modeling is a technique used for discovering the abstract 'topics' that occur in a collection of documents, which is useful for tasks such as text auto-categorization and opinion mining. In this paper, a commonsense knowledge based algorithm for document topic modeling is presented. In contrast to probabilistic models, the proposed approach does not involve training of any kind and does not depend on word co-occurrence or particular word distributions, making the algorithm effective on texts of any length and composition. 'Semantic atoms' are used to generate feature vectors for document concepts. These features are then clustered using group average agglomerative clustering, providing much improved performance over existing algorithms.||Source Title:||Proceedings of the 2nd International Workshop on Issues of Sentiment Discovery and Opinion Mining, WISDOM 2013 - Held in Conjunction with SIGKDD 2013||URI:||http://scholarbank.nus.edu.sg/handle/10635/116702||DOI:||10.1145/2502069.2502075|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on May 13, 2021
checked on May 16, 2021
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.