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Volumn 8835, Issue , 2014, Pages 279-286

Radical-enhanced chinese character embedding

Author keywords

Chinese character embedding; Neural network; Radical

Indexed keywords

EMBEDDINGS; LEARNING SYSTEMS; NEURAL NETWORKS; SEMANTICS;

EID: 84910113012     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-12640-1_34     Document Type: Conference Paper
Times cited : (113)

References (20)
  • 1
    • 56449095373 scopus 로고    scopus 로고
    • A unified architecture for natural language processing: Deep neural networks with multitask learning
    • ACM
    • Collobert, R., Weston, J.: A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167. ACM (2008)
    • (2008) Proceedings of the 25th International Conference on Machine Learning , pp. 160-167
    • Collobert, R.1    Weston, J.2
  • 2
    • 85083951332 scopus 로고    scopus 로고
    • Efficient estimation of word representations in vector space
    • Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR (2013)
    • (2013) ICLR
    • Mikolov, T.1    Chen, K.2    Corrado, G.3    Dean, J.4
  • 3
    • 84943739276 scopus 로고    scopus 로고
    • Feature-based neural language model and chinese word segmentation
    • Mansur, M., Pei, W., Chang, B.: Feature-based neural language model and chinese word segmentation. In: IJCNLP (2013)
    • (2013) IJCNLP
    • Mansur, M.1    Pei, W.2    Chang, B.3
  • 4
    • 84905255871 scopus 로고    scopus 로고
    • Deep learning for chinese word segmentation and pos tagging
    • Zheng, X., Chen, H., Xu, T.: Deep learning for chinese word segmentation and pos tagging. In: EMNLP (2013)
    • (2013) EMNLP
    • Zheng, X.1    Chen, H.2    Xu, T.3
  • 5
    • 84906928923 scopus 로고    scopus 로고
    • Exploiting multiple sources for open-domain hypernym discovery
    • Fu, R., Qin, B., Liu, T.: Exploiting multiple sources for open-domain hypernym discovery. In: EMNLP, pp. 1224–1234 (2013)
    • (2013) EMNLP , pp. 1224-1234
    • Fu, R.1    Qin, B.2    Liu, T.3
  • 8
    • 84906924577 scopus 로고    scopus 로고
    • Chinese parsing exploiting characters
    • Long Papers), Sofia, Bulgaria, Association for Computational Linguistics (August
    • Zhang, M., Zhang, Y., Che, W., Liu, T.: Chinese parsing exploiting characters. In: Proc. ACL (Volume 1: Long Papers), Sofia, Bulgaria, pp. 125–134. Association for Computational Linguistics (August 2013)
    • (2013) Proc. ACL , vol.1 , pp. 125-134
    • Zhang, M.1    Zhang, Y.2    Che, W.3    Liu, T.4
  • 9
    • 77952700189 scopus 로고    scopus 로고
    • From frequency to meaning: Vector space models of semantics
    • Turney, P.D., Pantel, P., et al.: From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research 37(1), 141–188 (2010)
    • (2010) Journal of Artificial Intelligence Research , vol.37 , Issue.1 , pp. 141-188
    • Turney, P.D.1    Pantel, P.2
  • 10
    • 80053495924 scopus 로고    scopus 로고
    • Word representations: A simple and general method for semi-supervised learning
    • Turian, J., Ratinov, L., Bengio, Y.: Word representations: a simple and general method for semi-supervised learning. ACL (2010)
    • (2010) ACL
    • Turian, J.1    Ratinov, L.2    Bengio, Y.3
  • 14
    • 84878180089 scopus 로고    scopus 로고
    • Improving word representations via global context and multiple word prototypes
    • Association for Computational Linguistics
    • Huang, E.H., Socher, R., Manning, C.D., Ng, A.Y.: Improving word representations via global context and multiple word prototypes. In: Proc. ACL, pp. 873–882. Association for Computational Linguistics (2012)
    • (2012) Proc. ACL , pp. 873-882
    • Huang, E.H.1    Socher, R.2    Manning, C.D.3    Ng, A.Y.4
  • 15
    • 84906924350 scopus 로고    scopus 로고
    • Learning sentiment-specific word embedding for twitter sentiment classification
    • Long Papers), Baltimore, Maryland, Association for Computational Linguistics, June
    • Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for twitter sentiment classification. In: Proc. ACL (Volume 1: Long Papers), Baltimore, Maryland, pp. 1555–1565. Association for Computational Linguistics (June 2014)
    • (2014) Proc. ACL , vol.1 , pp. 1555-1565
    • Tang, D.1    Wei, F.2    Yang, N.3    Zhou, M.4    Liu, T.5    Qin, B.6
  • 16
    • 85069972274 scopus 로고    scopus 로고
    • Better word representations with recursive neural networks for morphology
    • Luong, M.-T., Socher, R., Manning, C.D.: Better word representations with recursive neural networks for morphology. In: CoNLL 2013, p. 104 (2013)
    • (2013) CoNLL 2013 , pp. 104
    • Luong, M.-T.1    Socher, R.2    Manning, C.D.3
  • 18
    • 0015600423 scopus 로고
    • The viterbi algorithm
    • Forney Jr., G.D.: The viterbi algorithm. Proceedings of the IEEE 61(3), 268–278 (1973)
    • (1973) Proceedings of the IEEE , vol.61 , Issue.3 , pp. 268-278
    • Forney, G.D.1
  • 19
    • 84859904385 scopus 로고    scopus 로고
    • A cascaded linear model for joint chinese word segmentation and part-of-speech tagging
    • Citeseer
    • Jiang, W., Huang, L., Liu, Q., Lü, Y.: A cascaded linear model for joint chinese word segmentation and part-of-speech tagging. In: Proc. ACL. Citeseer (2008)
    • (2008) Proc. ACL
    • Jiang, W.1    Huang, L.2    Liu, Q.3    Lü, Y.4
  • 20
    • 85123581800 scopus 로고    scopus 로고
    • Improving chinese word segmentation and pos tagging with semi-supervised methods using large autoanalyzed data
    • Wang, Y., Jun’ichi Kazama, Y.T., Tsuruoka, Y., Chen, W., Zhang, Y., Torisawa, K.: Improving chinese word segmentation and pos tagging with semi-supervised methods using large autoanalyzed data. In: IJCNLP, pp. 309–317 (2011)
    • (2011) IJCNLP , pp. 309-317
    • Wang, Y.1    Jun’ichi Kazama, Y.T.2    Tsuruoka, Y.3    Chen, W.4    Zhang, Y.5    Torisawa, K.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.