{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:53:31Z","timestamp":1761094411925,"version":"build-2065373602"},"reference-count":15,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2017,10,24]],"date-time":"2017-10-24T00:00:00Z","timestamp":1508803200000},"content-version":"vor","delay-in-days":296,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["asistdl.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Proc. Assoc. Info. Sci. Tech."],"published-print":{"date-parts":[[2017,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>Currently, the internet has created many documents in languages other than English. People face challenges when seeking and using information; for example, non\u2010native English\u2010speaking students tend to have problems when utilizing libraries in North American universities. To help people efficiently organize information, bilingual documents clustering has advantages for practical utilization, it can divide documents into groups with the same topic and there is no need for a training dataset. Document representation and clustering models are two important parts in clustering. This paper compares four popular representation methods, vector space model (VSM), latent semantic indexing (LSI), latent Dirichlet allocation (LDA) and doc2vec (D2V), together with four different types of clustering algorithms, K\u2010means++, BIRCH, DBSCAN and affinity propagation (AP) to identify appropriate combinations for bilingual documents clustering. Parallel corpus and comparable corpus are all used for the bilingual datasets. Experimental results show that, clustering performance varies when combining different representation methods with clustering algorithms. It's important to make good choice of models for better documents organization.<\/jats:p>","DOI":"10.1002\/pra2.2017.14505401056","type":"journal-article","created":{"date-parts":[[2017,10,24]],"date-time":"2017-10-24T03:35:36Z","timestamp":1508816136000},"page":"499-502","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Document representation and clustering models for bilingual documents clustering"],"prefix":"10.1002","volume":"54","author":[{"given":"Shutian","family":"Ma","sequence":"first","affiliation":[{"name":"Department of Information Management Nanjing University of Science and Technology  China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengzhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Information Management Nanjing University of Science and Technology  China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2017,10,24]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"Arthur D. &Vassilvitskii S.(2007).K\u2010means++: The advantages of careful seeding.Proceedings of the Proceedings of the eighteenth annual ACM\u2010SIAM symposium on discrete algorithms(pp.1027\u20131035)."},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1162\/jmlr.2003.3.4-5.993"},{"key":"e_1_2_9_4_1","unstructured":"Ester M. Kriegel H.\u2010P. Sander J. &Xu X.(1996).A density\u2010based algorithm for discovering clusters in large spatial databases with noise.Proceedings of the Kdd(pp.226\u2013231)."},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1136800"},{"key":"e_1_2_9_6_1","doi-asserted-by":"crossref","unstructured":"Kiela D. &Clark S.(2014).A systematic study of semantic vector space model parameters.Proceedings of the 2nd workshop on continuous vector space models and their compositionality (CVSC) at EACL(pp.21\u201330).","DOI":"10.3115\/v1\/W14-1503"},{"key":"e_1_2_9_7_1","unstructured":"Le Q. V. &Mikolov T.(2014). Distributed representations of sentences and documents.arXiv preprint arXiv:1405.4053."},{"key":"e_1_2_9_8_1","doi-asserted-by":"crossref","unstructured":"Ma S. Zhang C. &He D.(2016).Document representation methods for clustering bilingual documents.Proceedings of the Association for Information Science and Technology 53(1) 1\u201310.","DOI":"10.1002\/pra2.2016.14505301065"},{"key":"e_1_2_9_9_1","doi-asserted-by":"crossref","unstructured":"Papadimitriou C. H. Tamaki H. Raghavan P. &Vempala S.(1998).Latent semantic indexing: A probabilistic analysis.Proceedings of the 7th ACM SIGACT\u2010SIGMOD\u2010SIGART symposium on principles of database systems(pp.159\u2013168).","DOI":"10.1145\/275487.275505"},{"key":"e_1_2_9_10_1","doi-asserted-by":"crossref","unstructured":"Rangrej A. Kulkarni S. &Tendulkar A. V.(2011).Comparative study of clustering techniques for short text documents.Proceedings of the 20th international conference companion on world wide web(pp.111\u2013112).","DOI":"10.1145\/1963192.1963249"},{"key":"e_1_2_9_11_1","unstructured":"Rosenberg A. &Hirschberg J.(2007).V\u2010measure: A conditional entropy\u2010based external cluster evaluation measure.Proceedings of the EMNLP\u2010CoNLL(pp.410\u2013420)."},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/361219.361220"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acalib.2015.04.003"},{"key":"e_1_2_9_14_1","unstructured":"Xia Y. Tang G. Jin P. &Yang X.(2012).CLTC: A Chinese\u2010English cross\u2010lingual topic corpus.Proceedings of the LREC(pp.532\u2013537)."},{"key":"e_1_2_9_15_1","doi-asserted-by":"crossref","unstructured":"Xu H. Zeng W. Gui J. Qu P. Zhu X. &Wang L.(2015 15\u201317 Aug. 2015).Exploring similarity between academic paper and patent based on latent semantic analysis and vector space model.Proceedings of the 2015 12th International conference on fuzzy systems and knowledge discovery (FSKD)(pp.801\u2013805).","DOI":"10.1109\/FSKD.2015.7382045"},{"key":"e_1_2_9_16_1","doi-asserted-by":"crossref","unstructured":"Zhang T. Ramakrishnan R. &Livny M.(1996).BIRCH: An efficient data clustering method for very large databases.Proceedings of the ACM SIGMOD Record(pp.103\u2013114).","DOI":"10.1145\/235968.233324"}],"container-title":["Proceedings of the Association for Information Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fpra2.2017.14505401056","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/pra2.2017.14505401056","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/pra2.2017.14505401056","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/asistdl.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/pra2.2017.14505401056","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T16:28:14Z","timestamp":1761064094000},"score":1,"resource":{"primary":{"URL":"https:\/\/asistdl.onlinelibrary.wiley.com\/doi\/10.1002\/pra2.2017.14505401056"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,1]]},"references-count":15,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,1]]}},"alternative-id":["10.1002\/pra2.2017.14505401056"],"URL":"https:\/\/doi.org\/10.1002\/pra2.2017.14505401056","archive":["Portico"],"relation":{},"ISSN":["2373-9231","2373-9231"],"issn-type":[{"type":"print","value":"2373-9231"},{"type":"electronic","value":"2373-9231"}],"subject":[],"published":{"date-parts":[[2017,1]]},"assertion":[{"value":"2017-10-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}