{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T17:48:52Z","timestamp":1777139332578,"version":"3.51.4"},"reference-count":66,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2018,6,27]],"date-time":"2018-06-27T00:00:00Z","timestamp":1530057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"ERATO Kawarabayashi Large Graph Project from the Japan Science and Technology Agency"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2018,10,31]]},"abstract":"<jats:p>\n            Short and sparse texts such as tweets, search engine snippets, product reviews, and chat messages are abundant on the Web. Classifying such short-texts into a pre-defined set of categories is a common problem that arises in various contexts, such as sentiment classification, spam detection, and information recommendation. The fundamental problem in short-text classification is\n            <jats:italic>feature sparseness<\/jats:italic>\n            -- the lack of feature overlap between a trained model and a test instance to be classified. We propose\n            <jats:italic>ClassiNet<\/jats:italic>\n            -- a network of classifiers trained for predicting missing features in a given instance, to overcome the feature sparseness problem. Using a set of unlabeled training instances, we first learn binary classifiers as feature predictors for predicting whether a particular feature occurs in a given instance. Next, each feature predictor is represented as a vertex\n            <jats:italic>v<\/jats:italic>\n            <jats:sub>i<\/jats:sub>\n            in the ClassiNet, where a one-to-one correspondence exists between feature predictors and vertices. The weight of the directed edge\n            <jats:italic>e<\/jats:italic>\n            <jats:sub>ij<\/jats:sub>\n            connecting a vertex\n            <jats:italic>v<\/jats:italic>\n            <jats:sub>i<\/jats:sub>\n            to a vertex\n            <jats:italic>v<\/jats:italic>\n            <jats:sub>j<\/jats:sub>\n            represents the conditional probability that given\n            <jats:italic>v<\/jats:italic>\n            <jats:sub>i<\/jats:sub>\n            exists in an instance,\n            <jats:italic>v<\/jats:italic>\n            <jats:sub>j<\/jats:sub>\n            also exists in the same instance.\n          <\/jats:p>\n          <jats:p>\n            We show that ClassiNets generalize word co-occurrence graphs by considering implicit co-occurrences between features. We extract numerous features from the trained ClassiNet to overcome feature sparseness. In particular, for a given instance\n            <jats:italic>x<\/jats:italic>\n            , we find similar features from ClassiNet that did not appear in\n            <jats:italic>x<\/jats:italic>\n            , and append those features in the representation of\n            <jats:italic>x<\/jats:italic>\n            . Moreover, we propose a method based on graph propagation to find features that are indirectly related to a given short-text. We evaluate ClassiNets on several benchmark datasets for short-text classification. Our experimental results show that by using ClassiNet, we can statistically significantly improve the accuracy in short-text classification tasks, without having to use any external resources such as thesauri for finding related features.\n          <\/jats:p>","DOI":"10.1145\/3201578","type":"journal-article","created":{"date-parts":[[2018,6,27]],"date-time":"2018-06-27T12:14:36Z","timestamp":1530101676000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["ClassiNet -- Predicting Missing Features for Short-Text Classification"],"prefix":"10.1145","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4476-7003","authenticated-orcid":false,"given":"Danushka","family":"Bollegala","sequence":"first","affiliation":[{"name":"University of Liverpool, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincent","family":"Atanasov","sequence":"additional","affiliation":[{"name":"University of Liverpool, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takanori","family":"Maehara","sequence":"additional","affiliation":[{"name":"RIKEN Center for Advanced Intelligence Project, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ken-Ichi","family":"Kawarabayashi","sequence":"additional","affiliation":[{"name":"National Institute of Informatics, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,6,27]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1327452.1327494"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the Association for Computational Linguistics (ACL\u201907)","author":"Blitzer John","year":"2007","unstructured":"John Blitzer , Mark Dredze , and Fernando Pereira . 2007 . 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