[1] |
SUN A X, LIM E P, NG W K. Web classiˉcation usingsupport vector machine [J]. Proceedings of the 4th In-ternational Workshop on Web Information and DataManagement (WIDM 2002). Virginia: ACM, 2002: 1-4.
|
[2] |
SHIH L K, KARGER D R. Using URLs and tablelayout for Web classiˉcation tasks [C]// InternationalConference on World Wide Web. New York: ACM,2004: 193-202.
|
[3] |
CRISTO M, CALADO P, DE MOURA E S, et al.Link information as a similarity measure inWeb classi-ˉcation [C]//International Symposium on String Pro-cessing and Information Retrieval. Manaus: Springer,2003: 43-55.
|
[4] |
ANH N T K, THANH V M, LINH N V. E±cient la-bel propagation for classiˉcation on information net-works [C]//Symposium on Information & Communi-cation Technology. Ha Long: ACM, 2012: 41-46.
|
[5] |
DUAN Q G, MIAO D Q, JIN K M. A rough setapproach to classifying Web page without negativeexamples [C]//Paciˉc-Asia Conference on Advancesin Knowledge Discovery and Data Mining. Nanjing:Springer, 2007: 481-488.
|
[6] |
KIM S M, PANTEL P, DUAN L, et al. Improv-ing web page classiˉcation by label-propagation overclick graphs [C]//ACM Conference on Informationand Knowledge Management. Hong Kong: ACM, 2009:572-576.
|
[7] |
NIE L, HUA Z G, HE X F, et al. Learning document la-bels from enriched click graphs [C]//the IEEE Interna-tional Conference on Data Mining Workshops. Sydney:IEEE, 2010: 57-64.
|
[8] |
LI X, WANG Y Y, ACERO A. Learning query intentfrom regularized click graphs [C]// The InternationalACM SIGIR Conference. Singapore: ACM, 2008: 339-346.
|
[9] |
ZHANG Z Y, NASRAOUI O. Mining searchengine query logs for query recommendation[C]//International Conference on World WideWeb. Edinburgh: ACM, 2006: 1039-1040.
|
[10] |
ZHU X J, GHAHRAMANI Z B. Learning from labeled and unlabeled data with label propagation [R].Pittsburgh: Carnegie Mellon University, 2002.
|
[11] |
HINTON G E. Learning distributed representations ofconcepts [C]//Proceedings of the Eighth Annual Con-ference of the Cognitive Science Society. Amherst, MA:[s.n.], 1986: 1-12.
|
[12] |
BENGIO Y, SCHWENK H, SEN?ECAL J S, et al.Neural probabilistic language models [J]. Innovationsin Machine Learning: Theory and Applications, 2006,194: 137-186.
|
[13] |
MIKOLOV T, CHEN K, CORRADO G, etal. E±cient estimation of word representa-tions in vector space [EB/OL].(2016-06-06).https://arxiv.org/abs/1301.3781v1.
|
[14] |
MIKOLOV T, KARAFIAT M, BURGET L, etal. Recurrent neural network based language model[C]//Conference of the International Speech Commu-nication Association. Makuhari: ISCA, 2010: 1045-1048.
|
[15] |
COLLOBERT R, WESTON J, BOTTOU L, et al.Natural language processing (almost) from scratch [J].Journal of Machine Learning Research, 2011, 12(1):2493-2537.
|
[16] |
MIKOLOV T, LE Q V, SUTSKEVER I.Exploiting similarities among languages formachine translation [EB/OL]. (2016-06-06).https://arxiv.org/abs/1309.4168.
|
[17] |
FROME A, CORRADO G S, SHLENS J, et al.DeVise: A deep visual-semantic embedding model[C]//Conference on Neural Information ProcessingSystems. [s.l.]: IEEE, 2013: 2121-2129.
|
[18] |
SOCHER R, CHEN D Q, MANNING C D, et al. Reasoning with neural tensor networks for knowledge basecompletion [C]//Advances in Neural Information Processing Systems. South Lake Tahoe: NIPS, 2013: 926-934.
|
[19] |
TANG D, WEI F, YANG N, et al. Learning sentimentspeciˉc word embedding for twitter sentiment classiˉcation [C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.Baltimore, Maryland: Association for ComputationalLinguistics, 2014: 1555-1565.
|
[20] |
SOCHER R, HUVAL B, MANNING C D, et al.Semantic compositionality through recursive matrixvector spaces [C]//Joint Conference on EmpiricalMethods in Natural Language Processing and Computational Natural Language Learning. Jeju Island: [s.n.],2012: 1201-1211.
|
[21] |
WHITE L, TOGNERI R, LIU W, et al. How well sentence embeddings capture meaning [C]//AustralasianDocument Computing Symposium. Parramatta: ACM,2015: 1-8.
|
[22] |
MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases andtheir compositionality [J]. Advances in neural information processing systems, 2013, 26: 3111-3119.
|
[23] |
YANG H B, HU Q M, HE L. Learning topic-orientedword embedding for query classiˉcation [C]//Advancesin Knowledge Discovery and Data Mining. [s.l.]:Springer International Publishing Switzerland, 2015:188-198.
|
[24] |
JIANG S, HU Y N, KANG C S, et al. Learning queryand document relevance from a Web-scale click graph[C]//The International ACM SIGIR Conference. Pisa:ACM, 2016: 185-194.
|