caa a22 4500 605468265 CHVBK 20210128100315.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00500-014-1372-z doi (NATIONALLICENCE)springer-10.1007/s00500-014-1372-z Statistical word sense aware topic models [Elektronische Daten] [Guoyu Tang, Yunqing Xia, Jun Sun, Min Zhang, Thomas Zheng] LDA has been proved effective in modeling the semantic relation between surface words. This semantic information in the document collection is useful to measure the topic distribution for a document. In general, a surface word may significantly contribute to several topics in a document collection. LDA measures the contribution of a surface word to each topic and considers a surface word to be identical across all documents. However, a surface word may present different signatures in different contexts, i.e., polysemous words can be used with different senses in different contexts. Intuitively, disambiguating word senses for topic models can enhance their discriminative capabilities. In this work, we propose a joint model to automatically induce document topics and word senses simultaneously. Instead of using some pre-defined word sense resources, we capture the word sense information via a latent variable and directly induce them in a fully unsupervised manner from the corpora. Experimental results show that the proposed joint model outperforms the baselines significantly in document clustering and improves the word sense induction as well against a standalone non-parametric model. Springer-Verlag Berlin Heidelberg, 2014 Topic modeling nationallicence Word sense induction nationallicence Document representation nationallicence Document clustering nationallicence Tang Guoyu Department of Computer Science and Technology, TNList, Tsinghua University, Beijing, China aut Xia Yunqing Department of Computer Science and Technology, TNList, Tsinghua University, Beijing, China aut Sun Jun Institute for Infocomm Research, A-STAR, Singapore, Singapore aut Zhang Min Soochow University, Suzhou, China aut Zheng Thomas Department of Computer Science and Technology, TNList, Tsinghua University, Beijing, China aut Soft Computing Springer Berlin Heidelberg 19/1(2015-01-01), 13-27 1432-7643 19:1<13 2015 19 500 https://doi.org/10.1007/s00500-014-1372-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1372-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Tang Guoyu Department of Computer Science and Technology, TNList, Tsinghua University, Beijing, China aut NATIONALLICENCE 700 1- Xia Yunqing Department of Computer Science and Technology, TNList, Tsinghua University, Beijing, China aut NATIONALLICENCE 700 1- Sun Jun Institute for Infocomm Research, A-STAR, Singapore, Singapore aut NATIONALLICENCE 700 1- Zhang Min Soochow University, Suzhou, China aut NATIONALLICENCE 700 1- Zheng Thomas Department of Computer Science and Technology, TNList, Tsinghua University, Beijing, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/1(2015-01-01), 13-27 1432-7643 19:1<13 2015 19 500