Dedre Gentner

Alice Gabrielle Twight Professor of Psychology & Education


Curriculum vitae



(847)467-1272


Department of Psychology

Northwestern University



Relational Words Have High Metaphoric Potential Semantic Signatures for Example-based Linguistic Metaphor Detection Automatic Metaphor Detection Using Large-scale Lexical Resources and Conventional Metaphor Ex- Traction Cross-lingual Metaphor Detection Using Common Semantic Features Identifying Meta


Journal article


Ekaterina Shutova, S. Argamon, J. Barnden, Gemma Boleda, Ted Briscoe, S. Clark, Anna Feldman, J. Feldman, Michael Flor, Eugenie Giesbrecht, Valia Kordoni, A. Korhonen, Mark G. Lee, Patricia Lichtenstein, James H. Martin, A. Musolff, S. Narayanan, T. Poibeau, T. Veale, Andreas Vlachos, Anja Jamrozik, Eyal Sagi, Micah B. Goldwater, Michael Mohler, D. Bracewell, Marc T. Tomlinson, David R Hinote, Y. Wilks, Adam Dalton, James F. Allen, Lucian, Dirk Hovy, Shashank Shrivastava, Sujay Kumar Jauhar, Mrinmaya Sachan, Kartik Goyal, Huying Li, Whitney E. Sanders, Eduard, Ilana Heintz, Ryan Gabbard, M. Srivastava, D. Barner, Donald Black, Majorie Friedman, T. Strzalkowski, G. Broadwell, Sarah M. Taylor, L. Feldman, Samira Shaikh, Ting Liu, B. Yamrom, Kit Cho, Umit Boz, Ignacio Cases, Kyle Elliot, Yulia Badryzlova, N. Shekhtman, Yekaterina Isaeva, R. Kerimov, Vii, Beata Beigman Klebanov, D. Gentner, Lucian Galescu, Yulia Tsvetkov, E. Mukomel, A. Gershman, E. Hovy, Marissa Friedman, R. Weischedel, Jonathan Dunn
2013

Semantic Scholar
Cite

Cite

APA   Click to copy
Shutova, E., Argamon, S., Barnden, J., Boleda, G., Briscoe, T., Clark, S., … Dunn, J. (2013). Relational Words Have High Metaphoric Potential Semantic Signatures for Example-based Linguistic Metaphor Detection Automatic Metaphor Detection Using Large-scale Lexical Resources and Conventional Metaphor Ex- Traction Cross-lingual Metaphor Detection Using Common Semantic Features Identifying Meta.


Chicago/Turabian   Click to copy
Shutova, Ekaterina, S. Argamon, J. Barnden, Gemma Boleda, Ted Briscoe, S. Clark, Anna Feldman, et al. “Relational Words Have High Metaphoric Potential Semantic Signatures for Example-Based Linguistic Metaphor Detection Automatic Metaphor Detection Using Large-Scale Lexical Resources and Conventional Metaphor Ex- Traction Cross-Lingual Metaphor Detection Using Common Semantic Features Identifying Meta” (2013).


MLA   Click to copy
Shutova, Ekaterina, et al. Relational Words Have High Metaphoric Potential Semantic Signatures for Example-Based Linguistic Metaphor Detection Automatic Metaphor Detection Using Large-Scale Lexical Resources and Conventional Metaphor Ex- Traction Cross-Lingual Metaphor Detection Using Common Semantic Features Identifying Meta. 2013.


BibTeX   Click to copy

@article{ekaterina2013a,
  title = {Relational Words Have High Metaphoric Potential Semantic Signatures for Example-based Linguistic Metaphor Detection Automatic Metaphor Detection Using Large-scale Lexical Resources and Conventional Metaphor Ex- Traction Cross-lingual Metaphor Detection Using Common Semantic Features Identifying Meta},
  year = {2013},
  author = {Shutova, Ekaterina and Argamon, S. and Barnden, J. and Boleda, Gemma and Briscoe, Ted and Clark, S. and Feldman, Anna and Feldman, J. and Flor, Michael and Giesbrecht, Eugenie and Kordoni, Valia and Korhonen, A. and Lee, Mark G. and Lichtenstein, Patricia and Martin, James H. and Musolff, A. and Narayanan, S. and Poibeau, T. and Veale, T. and Vlachos, Andreas and Jamrozik, Anja and Sagi, Eyal and Goldwater, Micah B. and Mohler, Michael and Bracewell, D. and Tomlinson, Marc T. and Hinote, David R and Wilks, Y. and Dalton, Adam and Allen, James F. and Lucian and Hovy, Dirk and Shrivastava, Shashank and Jauhar, Sujay Kumar and Sachan, Mrinmaya and Goyal, Kartik and Li, Huying and Sanders, Whitney E. and Eduard and Heintz, Ilana and Gabbard, Ryan and Srivastava, M. and Barner, D. and Black, Donald and Friedman, Majorie and Strzalkowski, T. and Broadwell, G. and Taylor, Sarah M. and Feldman, L. and Shaikh, Samira and Liu, Ting and Yamrom, B. and Cho, Kit and Boz, Umit and Cases, Ignacio and Elliot, Kyle and Badryzlova, Yulia and Shekhtman, N. and Isaeva, Yekaterina and Kerimov, R. and Vii and Klebanov, Beata Beigman and Gentner, D. and Galescu, Lucian and Tsvetkov, Yulia and Mukomel, E. and Gershman, A. and Hovy, E. and Friedman, Marissa and Weischedel, R. and Dunn, Jonathan}
}

Abstract

ii Introduction Characteristic to all areas of human activity (from poetic to ordinary to scientific) and, thus, to all types of discourse, metaphor becomes an important problem for natural language processing. Its ubiquity in language has been established in a number of corpus studies and the role it plays in human reasoning has been confirmed in psychological experiments. This makes metaphor an important research area for computational and cognitive linguistics, and its automatic identification and interpretation indispensable for any semantics-oriented NLP application. The work on metaphor in NLP and AI started in the 1980s, providing us with a wealth of ideas on the structure and mechanisms of the phenomenon. The last decade witnessed a technological leap in natural language computation, whereby manually crafted rules gradually give way to more robust corpus-based statistical methods. This is also the case for metaphor research. In the recent years, the problem of metaphor modeling has been steadily gaining interest within the NLP community, with a growing number of approaches exploiting statistical techniques. Compared to more traditional approaches based on hand-coded knowledge, these more recent methods tend to have a wider coverage, as well as be more efficient, accurate and robust. However, even the statistical metaphor processing approaches so far often focused on a limited domain or a subset of phenomena. At the same time, recent work on computational lexical semantics and lexical acquisition techniques, as well as a wide range of NLP methods applying machine learning to open-domain semantic tasks, open many new avenues for creation of large-scale robust tools for recognition and interpretation of metaphor. This workshop is the first one focused on modelling of metaphor using NLP techniques. Recent related events include workshops on Computational Approaches to Figurative Language (NAACL 2007) and on Computational Approaches to Linguistic Creativity (NAACL 2009, NAACL 2010). We received 14 submissions and accepted 10. Each paper was carefully reviewed by at least 3 members of the Program Committee. The selected papers offer explorations into the following directions: (1) creation of metaphor-annotated datasets; (2) identification of new features that are useful for metaphor identification; (3) cross-lingual metaphor identification. The papers represent a variety of approaches to utilization and creation of datasets. While existing annotated corpora were used in some papers (Dunn, Tsvetkov et al), most papers describe creation of new annotated materials. Along with annotation guidelines adapted from the MIP and MIPVU procedures (Badryzlova et al), more intuitive …


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