메뉴 건너뛰기




Volumn 2, Issue , 2015, Pages 119-124

Exploiting image generality for lexical entailment detection

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; LINGUISTICS; SEMANTICS;

EID: 84944032131     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/v1/p15-2020     Document Type: Conference Paper
Times cited : (66)

References (33)
  • 1
  • 2
    • 84866860630 scopus 로고    scopus 로고
    • Using VI-sual information to predict lexical preference
    • Shane Bergsma and Randy Goebel. 2011. Using vi-sual information to predict lexical preference. In Proceedings ofRANLP, pages 399-105
    • (2011) Proceedings OfRANLP , pp. 399-405
    • Bergsma, S.1    Goebel, R.2
  • 3
    • 85120787167 scopus 로고    scopus 로고
    • Classifying taxonomic relations between pairs of wikipedia arti-cles
    • Or Biran and Kathleen McKeown. 2013. Classifying taxonomic relations between pairs of wikipedia arti-cles. In Proceedings ofUCNLP, pages 788-794
    • (2013) Proceedings OfUCNLP , pp. 788-794
    • Biran, O.1    McKeown, K.2
  • 4
    • 80053278031 scopus 로고    scopus 로고
    • Recognising tex-tual entailment with logical inference
    • Johan Bos and Katja Markert. 2005. Recognising tex-tual entailment with logical inference. In Proceed-ings ofEMNLP, pages 628-635
    • (2005) Proceed-ings OfEMNLP , pp. 628-635
    • Bos, J.1    Markert, K.2
  • 6
    • 85188826615 scopus 로고    scopus 로고
    • Context-theoretic semantics for natural language: An overview
    • Daoud Clarke. 2009. Context-theoretic semantics for natural language: An overview. In Proceedings of the GEMS 2009 Workshop, pages 112-119
    • (2009) Proceedings of the GEMS 2009 Workshop , pp. 112-119
    • Clarke, D.1
  • 7
    • 85198028989 scopus 로고    scopus 로고
    • ImageNet: A large-scale hier-archical image database
    • Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li. 2009. ImageNet: A large-scale hier-archical image database. In Proceedings of CVPR, pages 248-255
    • (2009) Proceedings of CVPR , pp. 248-255
    • Deng, J.1    Dong, W.2    Socher, R.3    Li, L.4    Li, K.5    Li, F.6
  • 8
    • 80052872247 scopus 로고    scopus 로고
    • Visual and semantic similarity in imagenet
    • Thomas Deselaers and Vittorio Ferrari. 2011. Visual and semantic similarity in imagenet. In Proceedings of CVPR, pages 1777-1784
    • (2011) Proceedings of CVPR , pp. 1777-1784
    • Deselaers, T.1    Ferrari, V.2
  • 9
    • 33745839880 scopus 로고    scopus 로고
    • Learning object categories from Google's image search
    • Robert Fergus, Li Fei-Fei, Pietro Perona, and Andrew Zisserman. 2005. Learning object categories from Google's image search. In Proceedings of ICCV, pages 1816-1823
    • (2005) Proceedings of ICCV , pp. 1816-1823
    • Fergus, R.1    Fei-Fei, L.2    Perona, P.3    Zisserman, A.4
  • 10
    • 84867306903 scopus 로고    scopus 로고
    • Integrating logical representations with prob-abilistic information using Markov logic
    • Dan Garrette, Katrin Erk, and Raymond Mooney. 2011. Integrating logical representations with prob-abilistic information using Markov logic. In Pro-ceedings oflWCS, pages 105-114
    • (2011) Pro-ceedings of LWCS , pp. 105-114
    • Garrette, D.1    Erk, K.2    Mooney, R.3
  • 11
    • 84859922478 scopus 로고    scopus 로고
    • The distributional in-clusion hypotheses and lexical entailment
    • M. Geffet and I. Dagan. 2005. The distributional in-clusion hypotheses and lexical entailment. In Pro-ceedings of ACL, pages 107-114
    • (2005) Pro-ceedings of ACL , pp. 107-114
    • Geffet, M.1    Dagan, I.2
  • 12
    • 84907371511 scopus 로고    scopus 로고
    • Measuring semantic content in distributional vec-tors
    • Aurelie Herbelot and Mohan Ganesalingam. 2013. Measuring semantic content in distributional vec-tors. In Proceedings of ACL, pages 440-145
    • (2013) Proceedings of ACL , pp. 440-445
    • Herbelot, A.1    Ganesalingam, M.2
  • 15
    • 84952650015 scopus 로고    scopus 로고
    • Learning image embeddings using convolutional neural networks for improved multi-modal semantics
    • Douwe Kiela and Leon Bottou. 2014. Learning image embeddings using convolutional neural networks for improved multi-modal semantics. In Proceedings of EMNLP, pages 36-15
    • (2014) Proceedings of EMNLP , pp. 36-45
    • Kiela, D.1    Bottou, L.2
  • 16
    • 84906926558 scopus 로고    scopus 로고
    • Improving multi-modal representa-tions using image dispersion: Why less is sometimes more
    • Douwe Kiela, Felix Hill, Anna Korhonen, and Stephen Clark. 2014. Improving multi-modal representa-tions using image dispersion: Why less is sometimes more. In Proceedings of ACL, pages 835-841
    • (2014) Proceedings of ACL , pp. 835-841
    • Kiela, D.1    Hill, F.2    Korhonen, A.3    Clark, S.4
  • 19
    • 84949768227 scopus 로고    scopus 로고
    • Identify-ing hypernyms in distributional semantic spaces
    • Alessandro Lenci and Giulia Benotto. 2012. Identify-ing hypernyms in distributional semantic spaces. In Proceedings of ∗SEM, pages 75-79
    • (2012) Proceedings of ∗sEM , pp. 75-79
    • Lenci, A.1    Benotto, G.2
  • 20
    • 84959876458 scopus 로고    scopus 로고
    • Do supervised distributional methods really learn lexical inference relations?
    • Omer Levy, Steffen Remus, Chris Biemann, and Ido Dagan. 2015. Do supervised distributional methods really learn lexical inference relations? In Proceed-ings ofNAACL
    • (2015) Proceed-ings OfNAACL
    • Levy, O.1    Remus, S.2    Biemann, C.3    Dagan, I.4
  • 22
    • 84976702763 scopus 로고
    • WordNet: A lexical database for English
    • George A. Miller. 1995. WordNet: A lexical database for English. In Communications of the ACM, vol-ume 38, pages 39-11
    • (1995) Communications of the ACM , vol.38 , pp. 39-41
    • Miller, G.A.1
  • 24
    • 84905715398 scopus 로고    scopus 로고
    • Distributional lexical entailment by topic coherence
    • Laura Rimell. 2014. Distributional lexical entailment by topic coherence. In Proceedings ofEACL, pages 511-519
    • (2014) Proceedings OfEACL , pp. 511-519
    • Rimell, L.1
  • 27
    • 84906930522 scopus 로고    scopus 로고
    • Learn-ing grounded meaning representations with autoen-coders
    • Carina Silberer and Mirella Lapata. 2014. Learn-ing grounded meaning representations with autoen-coders. In Proceedings of ACL, pages 721-732
    • (2014) Proceedings of ACL , pp. 721-732
    • Silberer, C.1    Lapata, M.2
  • 28
    • 0345414182 scopus 로고    scopus 로고
    • Video Google: A text retrieval approach to object match-ing in videos
    • Josef Sivic and Andrew Zisserman. 2003. Video Google: A text retrieval approach to object match-ing in videos. In Proceedings oflCCV, pages 1470-1477
    • (2003) Proceedings OflCCV , pp. 1470-1477
    • Sivic, J.1    Zisserman, A.2
  • 29
    • 80053255626 scopus 로고    scopus 로고
    • Literal and metaphorical sense iden-tification through concrete and abstract context
    • Peter D. Turney, Yair Neuman, Dan Assaf, and Yohai Cohen. 2011. Literal and metaphorical sense iden-tification through concrete and abstract context. In Proceedings ofEMNLP, pages 680-690
    • (2011) Proceedings OfEMNLP , pp. 680-690
    • Turney, P.D.1    Neuman, Y.2    Assaf, D.3    Cohen, Y.4
  • 30
    • 85119097733 scopus 로고    scopus 로고
    • Characterising measures of lexical distributional similarity
    • Julie Weeds, David Weir, and Diana McCarthy. 2004. Characterising measures of lexical distributional similarity. In Proceedings ofCOLING
    • (2004) Proceedings OfCOLING
    • Weeds, J.1    Weir, D.2    McCarthy, D.3
  • 32
  • 33
    • 70349521675 scopus 로고    scopus 로고
    • Bootstrap-ping distributional feature vector quality
    • M. Zhitomirsky-Geffet and I. Dagan. 2009. Bootstrap-ping distributional feature vector quality. Computa-tional Linguistics, 35(3):435461
    • (2009) Computa-tional Linguistics , vol.35 , Issue.3 , pp. 435461
    • Zhitomirsky-Geffet, M.1    Dagan, I.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.