Separating (non-)figurative weeds from wheat
While approaches developed to recognize
figurative expressions in discourse widely differ with respect to
their formalization, most of them aim for the identification of the
figurativeness as directly as possible. There is, however, another
promising starting point – to turn our back to figurative wheat and
attend to non-figurative weeds first, identifying and subsequently
eliminating them from further consideration. On the basis of a
methodological exercise consisting of several small-scale case
studies involving English and Croatian material, we claim that by
approaching metaphors in a negative way we can achieve a high
success rate while using considerably leaner tools. We also show
that the situation with conceptual metonymies seems to be very
different, i.e. searching for literal uses first and then for
metonymic ones, does not lead to the same success.
Article outline
- 1.Introduction
- 2.Some approaches to the recognition, identification and extraction
of figurative expressions
- 3.Turning our back to figurative wheat and attending to
non-figurative weeds first: Why and how?
- 4.A brief note on metonymies
- 5.Concluding remarks and outlook
-
Notes
-
References
References (32)
References
Berber Sardinha, Tony. (2008). Metaphor
probabilities in
corpora. In M. S. Zanotto, L. Cameron, & M. C. Cavalcanti (Eds.), Confronting
metaphor in use: An applied linguistic
approach (pp. 127–147). Amsterdam/Philadelphia: John Benjamins.
Berber Sardinha, T. (2012). An
assessment of metaphor retrieval
methods. In F. MacArthur, J. L. Oncins-Martínez, M. Sánchez-García, & A. M. Piquer Píriz (Eds.), Metaphor
in use: Context, culture, and
communication (pp. 21–50). Amsterdam/Philadelphia: John Benjamins.
Cameron, L., & Deignan, A. (2003). Combining
large and small corpora to investigate tuning devices around
metaphor in spoken
discourse. Metaphor and
Symbol 18 (3): 149–160.
Colston, H. L. (2015). Using Figurative Language. New York: Cambridge University Press.
Coulson, S., & Matlock, T. (2001). Metaphor
and the space structuring
model. Metaphor and
Symbol, 16(3–4): 295–316.
Cruse, D. A. (2001). The
lexicon. In M. Aronoff, & J. Rees-Millier (Eds.), The
Handbook of
Linguistics (pp. 238–264). Oxford: Blackwell.
Dancygier, B., & Sweetser, E. (2014). Figurative Language. Cambridge: Cambridge University Press.
Dodge, E., Hong, J., & Stickles, E. (2015). MetaNet:
Deep semantic automatic metaphor
analysis. Third Workshop on
Metaphor in NLP 2015. Denver, Colorado, USA, 5 June
2015: 40–49.
Gabrilovich, E., & Markovitch, S. (2007). Computing
semantic relatedness using wikipedia-based explicit semantic
analysis. Proceedings of the
International Joint Conference on Artificial
Intelligence, 1606–1611.
Gibbs, R. W., Jr & Colston, H. L. (2012). Interpreting Figurative Meaning. Cambridge: Cambridge University Press.
Goatly, A. (1997). The
Language of
metaphors. London: Routledge.
Handl, S. (2011). The
Conventionality of figurative language: A usage-based
study. Tübingen: Narr Francke Attempto Verlag.
Kövecses, Z. (2000). The
scope of
metaphor. In A. Barcelona (Ed.), Metaphor
and metonymy at the crossroads: A cognitive
perspective (79–92). Berlin: Mouton de Gruyter.
Kövecses, Z. (2015). Two
ways of studying emotion metaphors in cognitive
linguistics. Paper presented
at the workshop Emotion
Concepts in
Use
, June 25–26,
2015, Heinrich-Heine-University,
Düsseldorf.
Leong, C., Beigman Klebanov, B. and Shutova, E. (2018). A
Report on the 2018 VUA Metaphor Detection Shared
Task. Proceedings of the
Workshop on Figurative Language
Processing. Association for
Computational Linguistics.
Li, H., Zhu, K. Q., & Wang, H. (2013). Data-driven
metaphor recognition and
explanation. Transactions of
the Association for Computational
Linguistics, 1, 379–390.
Markert, K., & Nissim, M. (2006). Metonymic
proper names: A corpus-based
account. In A. Stefanowitsch, & S.Th. Gries (Eds.), Corpus-based
approaches to metaphor and
metonymy (pp. 152–174). Berlin: Mouton de Gruyter.
Nissim, M., & Markert, K. (2003). Syntactic
features and word similarity for supervised metonymy
resolution. Proceedings of
the 41st Annual Meeting of the Association for Computational
Linguistics (ACL2003).
Parasuraman, A., Grewal, D., & Krishnan, R. (2004). Marketing
research. Boston: Houghton Mifflin.
Peirsman, Y. (2006). What’s
in a name? The automatic recognition of metonymical location
names. Proceedings of the
EACL-2006 Workshop on Making Sense of Sense: Bringing
Psycholinguistics and Computational Linguistics
Together (pp. 25–32). Trento: ACL.
Shutova, E. (2009). Sense-based
interpretation of logical metonymy using a statistical
method. Proceedings of the
ACL-IJCNLP 2009 Student Research
Workshop, Singapore, 1–9.
Shutova, E., Kaplan, J., Teufel, S., & Korhonen, A. (2013). A
computational model of logical
metonymy. ACM Transactions on
Speech and Language
Processing, 10(3), 1–28.
Shutova, E., & Sun, L. (2013). Unsupervised
metaphor identification using hierarchical graph
factorization
clustering. Proceedings of
the 2013 Conference of the North American Chapter of the
Association for Computational Linguistics: Human Language
Technologies, 2013, 978–988.
Shutova, E., Sun, L. & Korhonen, A. (2010). Metaphor
Identification Using Verb and Noun
Clustering. In Proceedings
of COLING
2010, Beijing: China.
Shutova, E., Teufel, S., & Korhonen, A. (2013). Statistical
metaphor
processing. Computational
Linguistics, 39(2), 301–353.
Stefanowitsch, A. (2004).
happiness
in English and German: A metaphorical-pattern
analysis. In M. Achard, & S. Kemmer (Eds.), Language,
culture, and
mind (pp. 137–149). Stanford, Calif.: CSLI Publications.
Stefanowitsch, A. (2006). Words
and their metaphors: A corpus-based
approach. In A. Stefanowitsch, & S. Th. Gries (Eds.), Corpus-based
approaches to metaphor and
metonymy (pp. 63–105). Berlin: Mouton de Gruyter.
Steen, G. J., Dorst, A. G., Herrmann, J. B., Kaal, A. A., Krennmayr, T., & Pasma, T. (2010). A
Method for Linguistic Metaphor Identification: From MIP to
MIPVU. Amsterdam/Philadelphia: John Benjamins.
Wallington, A. M., Barnden, J. A., Barnden, M. A., Ferguson, F. J., & Glaseby, S. R. (2003). Metaphoricity
signals: A corpus-based
investigation. Birmingham: School of Computer Science, University of Birmingham, U.K.
Winston, M. E., Chaffin, R., & Herrmann, D. (1987). A
taxonomy of part–whole
relations. Cognitive
Science, 11(4), 417–444.
Cited by (3)
Cited by three other publications
Brglez, Mojca, Omnia Zayed & Paul Buitelaar
2024.
TCMeta: a multilingual dataset of COVID tweets for relation-level metaphor analysis.
Language Resources and Evaluation
Farkhani, Sadaf, Søren Kelstrup Skovsen, Mads Dyrmann, Rasmus Nyholm Jørgensen & Henrik Karstoft
2021.
Weed Classification Using Explainable Multi-Resolution Slot Attention.
Sensors 21:20
► pp. 6705 ff.
This list is based on CrossRef data as of 7 september 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers.
Any errors therein should be reported to them.