Veale, T., Shutova, E., Beigman Klebanov, B. (2016). Metaphor: A Computational Perspective

References

1. Barnden, J.
   (2006) Artificial intelligence, figurative language and cognitive linguistics. InG. Kristiansen, M. Achard, R. Dirven, & F. J. Ruiz de Mendoza Ibáñez (Eds.), Cognitive linguistics: Current applications and future perspectives (pp.431–459). Berlin: Mouton de Gruyter. doi:  10.1515/9783110197761
   https://doi.org/10.1515/9783110197761 [Google Scholar]
2. Barnden, J., & Lee, M.
   (2002) An artificial intelligence approach to metaphor understanding. Theoria et Historia Scientiarum, 6(1), 399–412. doi:  10.12775/ths.2002.017
   https://doi.org/10.12775/ths.2002.017 [Google Scholar]
3. Beigman Klebanov, B., Wee Leong, C., Heilman, M., & Flor, M.
   (2014) Different texts, same metaphors: Unigrams and beyond. InB. Beigman Klebanov, E. Shutova, & P. Lichtenstein (Eds.), Proceedings of the 2nd Workshop on Metaphor in NLP (pp.11–17). Baltimore, MD: Association for Computational Linguistics. doi:  10.3115/v1/W14‑2302
   https://doi.org/10.3115/v1/W14-2302 [Google Scholar]
4. Burstein, J., Sabatini, J., Shore, J., Moulder, B., & Lentini, J.
   (2013) A user study: Technology to increase teachers’ linguistic awareness to improve instructional language support for English language learners. InL. Rello, H. Saggion, & R. Beaza-Yates (Eds.), Proceedings of the Workshop on Natural Language Processing for Improving Textual Accessibility (pp.1–10). Atlanta, GA: Association for Computational Linguistics.
   [Google Scholar]
5. Cucerzan, S.
   (2007) Large-scale named entity disambiguation on Wikipedia data. InProceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (pp.708–716). Prague, Czech Republic: Association for Computational Linguistics.
   [Google Scholar]
6. Dong, Z., & Dong, Q.
   (2006) HowNet and the computation of meaning. Singapore: World Scientific. doi:  10.1142/9789812774675
   https://doi.org/10.1142/9789812774675 [Google Scholar]
7. Fellbaum, C.
   (Ed.) (1998) WordNet: An electronic lexical database. Cambridge, MA: MIT Press. 10.7551/mitpress/7287.001.0001
   https://doi.org/10.7551/mitpress/7287.001.0001 [Google Scholar]
8. Finkel, J., Grenager, T., & Manning, C.
   (2005) Incorporating non-local information into information extraction systems by Gibbs sampling. InK. Knight, H. T. Ng, & K. Oflazer (Eds.), Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005) (pp.363–370). Ann Arbor, MI: Association for Computational Linguistics. doi: 10.3115/1219840.1219885.62
   https://doi.org/10.3115/1219840.1219885.62 [Google Scholar]
9. Jang, H., Piergallini, M., Wen, M., & Rose, C.
   (2014) Conversational metaphors in use: Exploring the contrast between technical and everyday notions of metaphor. InB. Beigman Klebanov, E. Shutova, & P. Lichtenstein (Eds.), Proceedings of the 2nd Workshop on Metaphor in NLP (pp.1–10). Baltimore, MD: Association for Computational Linguistics. doi:  10.3115/v1/W14‑2301
   https://doi.org/10.3115/v1/W14-2301 [Google Scholar]
10. Martin, J.
    (1990) A computational model of metaphor interpretation. San Diego, CA: Academic Press Professional Inc.
    [Google Scholar]
11. Peters, W., & Peters, I.
    (2000) Lexicalised systematic polysemy in WordNet. InProceedings of the 2nd International Conference on Language Resources and Evaluation (LERC 2000). Athens, Greece: European Languages Resources Association.
    [Google Scholar]
12. Pragglejaz Group
    Pragglejaz Group (2007) MIP: A method for identifying metaphorically used words in discourse. Metaphor and Symbol, 22(1), 1–39. doi:  10.1207/s15327868ms2201_1
    https://doi.org/10.1207/s15327868ms2201_1 [Google Scholar]
13. Ratinov, L., & Roth, D.
    (2009) Design challenges and misconceptions in named entity recognition. InS. Stevenson & X. Carreras (Eds.), Proceedings of the 13th Conference on Computational Natural Language Learning (CoNLL-2009) (pp.147–155). Boulder, Colorado: Association for Computational Linguistics. doi:  10.3115/1611528.1611530
    https://doi.org/10.3115/1611528.1611530 [Google Scholar]
14. Shutova, E.
    (2013) Metaphor identification as interpretation. InM. Diab, T. Baldwin, & M. Baroni (Eds.), Proceedings of the 2nd Joint Conference on Lexical and Computational Semantics (*SEM 2013) (pp.276–285). Atlanta, GA: Association for Computational Linguistics.
    [Google Scholar]
15. Shutova, E., & Teufel, S.
    (2010) Metaphor corpus annotated for source-target domain mappings. InN. Calzolari, K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, & D. Tapias (Eds.) Proceedings of the 7th International Conference on Language Resources and Evaluation (LERC 2010) (pp.3255–3261). Valletta, Malta: European Languages Resources Association.
    [Google Scholar]
16. Steen, G., Dorst, A., Herrmann, B., Kaal, A., Krennmayr, T., & Pasma, T.
    (2010) A method for linguistic metaphor identification: From MIP to MIPVU. Amsterdam: John Benjamins. doi:  10.1075/celcr.14
    https://doi.org/10.1075/celcr.14 [Google Scholar]
17. Strapparava, C.
    (2018) [Review of the bookMetaphor: A computational perspective, byT. Veale, E. Shutova, & B. Beigman Klebanov]. Computational Linguistics, 44(1), 191–192. doi:  10.1126/science.290.5495.1304
    https://doi.org/10.1126/science.290.5495.1304 [Google Scholar]
18. Toutanova, K., Klein, D., Manning, C., & Singer, Y.
    (2003) Feature-rich part-of-speech tagging with a cyclic dependency network. InProceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (pp.252–259). Edmond, Canada: Association for Computational Linguistics. doi:  10.3115/1073445.1073478
    https://doi.org/10.3115/1073445.1073478 [Google Scholar]
19. Turney, P., Neuman, Y., Assaf, D., & Cohen, Y.
    (2011) Literal and metaphorical sense identification through concrete and abstract context. InR. Barzilay & M. Johnson (Eds.), Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2011) (pp.680–690). Edinburgh, UK: Association for Computational Linguistics.
    [Google Scholar]
20. Veale, T.
    (2011) Creative language retrieval: A robust hybrid of information retrieval and linguistic creativity. InY. Matsumoto & R. Mihalcea (Eds.), Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL 2011) (pp.278–287). Portland, OR: Association for Computational Linguistics.
    [Google Scholar]
21. Veale, T.
    (2012) A context-sensitive, multi-faceted model of lexico-conceptual affect. InH. Li, C.-Y. Lin, M. Osborne, G. G. Lee, & J. C. Park (Eds.), Proceedings of the 50th Annual Conference of the Association for Computational Linguistics (ACL 2012) (pp.75–79). Jeju Island, Korea: Association for Computational Linguistics.
    [Google Scholar]
22. Veale, T.
    (2014) Coming good and breaking bad: Generating transformative character arcs for use in compelling stories. InS. Colton, D. Ventura, N. Lavrač, & M. Cook (Eds.), Proceedings of the 5th International Conference on Computational Creativity (ICCC-2014) (pp.239–246). Ljubljana, Slovenia: Jožef Stefan Institute.
    [Google Scholar]
23. Veale, T., Valitutti, A., & Li, G.
    (2015) Twitter: The best of bot worlds for automated wit. InN. Streitz & P. Markopoulos (Eds.), Proceedings of the 3rd International Conference on Distributed, Ambient and Pervasive Interactions at the 17th International Conference on Human-Computer Interactions (DAPI/HCII-2015) (pp.689–699). Los Angeles, CA. doi:  10.1007/978‑3‑319‑20804‑6_63
    https://doi.org/10.1007/978-3-319-20804-6_63 [Google Scholar]
24. Wilks, Y., Galescu, L., Allen, J., & Dalton, A.
    (2013) Automatic metaphor detection using large-scale lexical resources and conventional metaphor extraction. InE. Shutova, B. Beigman Klebanov, J. Tetreault, & Z. Kozareva (Eds.), Proceedings of the 1st Workshop on Metaphor in NLP (pp.36–44). Atlanta, GA: Association for Computational Linguistics.
    [Google Scholar]