Chapter published in:
Producing Figurative Expression: Theoretical, experimental and practical perspectivesEdited by John Barnden and Andrew Gargett
[Figurative Thought and Language 10] 2020
► pp. 419–448
Metaphor generation through context sensitive distributional semantics
Stephen McGregor | ction.ai
Matthew Purver | Cognitive Science Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Ljubljana | Department of Knowledge Technologies, InstitutJožef Stefan, Ljubljana
Geraint Wiggins | Cognitive Science Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Ljubljana | Vrije Universiteit Brussel
In this paper, we outline a preliminary
methodology for generating metaphor based on contextual projections
of representations built up through a statistical analysis of a
large-scale linguistic corpus. These projections involve defining
subspaces of co-occurrence statistics in which we show that
metaphors can be modelled as mappings between congruent regions of
semantic representations. We offer this methodology as an empirical
implementation pointing towards a resolution of theoretical stances,
at times incompatible, construing metaphor as on the one hand an
artefact of underlying cognitive processes and on the other hand a
product of the environmentally situated generation of ephemeral
conceptual schemes.
Keywords: metaphor generation, pragmatics, distributional semantics, natural language processing, context sensitive semantic modelling
Article outline
- 1.Introduction
- 2. Language in minds, minds in the world
- 3.Computational approaches to metaphor
- 4. Semantics in perspective
- 5.Projecting metaphorical mappings
- 6.Finding coherent subspaces
- 7.The way forward
- 8.Conclusion
-
Notes -
References
Published online: 17 December 2020
https://doi.org/10.1075/ftl.10.15mcg
https://doi.org/10.1075/ftl.10.15mcg
References
References
Agres, K. R., McGregor, S., Rataj, K., Purver, M., & Wiggins, G. A.
(2016) Modeling
metaphor perception with distributional semantics vector
space
models. In
Workshop
on Computational Creativity, Concept Invention, and
General
Intelligence
.
Barnden, J. A., & Lee, M. G.
(1999) An
implemented context system that combines belief reasoning,
metaphor-based reasoning and uncertainty
handling. In
Modeling
and Using Context: Second International and
Interdisciplinary
Conference
(pp. 28– 41).
Baroni, M., & Zamparelli, R.
(2010) Nouns
are vectors, adjectives are matrices: Representing
adjective-noun constructions in semantic
space. In
Proceedings
of the 2010 Conference on Empirical Methods in Natural
Language
Processing
(pp. 1183– 1193).
Barsalou, L. W.
Caliskan, A., Bryson, J. J., & Narayanan, A.
Carston, R.
Clark, A.
Clark, S.
Coecke, B., Sadrzadeh, M., & Clark, S.
Davidson, D.
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K.
(2019) BERT:
Pre-training of deep bidirectional transformers for language
understanding. In
Proceedings
of the 2019 Conference of the North American Chapter of
the Association for Computational
Linguistics
(pp. 4171–4186).
Ethayarajh, K.
(2019) How
contextual are contextualized word representations?
comparing the geometry of BERT, ELMo, and GPT-2
embeddings. In
Proceedings
of the 2019 Conference on Empirical Methods in Natural
Language Processing and the 9th International Joint
Conference on Natural Language
Processing
(pp. 55–65).
Fauconnier, G., & Turner, M. B.
Gargett, A., & J. Barnden
(2013) Gen-meta:
Generating metaphors using a combination of AI reasoning and
corpus-based modeling of formulaic
expressions. In
Proceedings
of TAAI
2013
.
Gentner, D.
Gibbs Jr., R. W., & Tendahl, M.
Grefenstette, E., Sadrzadeh, M., Clark, S., Coecke, B., & Pulman, S.
Grice, H. P.
Gutiérrez, E. D., Shutova, E., Marghetis, T., & Bergen, B. K.
Kartsaklis, D., & Sadrzadeh, M.
(2013) Prior
disambiguation of word tensors for constructing sentence
vectors. In
Proceedings
of the 2013 Conference on Empirical Methods in Natural
Language
Processing
(pp. 1590–1601).
Kintsch, W.
Levinson, S. C.
Mason, Z. J.
McGregor, S., Agres, K., Purver, M., & Wiggins, G.
McGregor, S., Jezek, E., Purver, M., & Wiggins, G.
(2017) A
geometric method for detecting semantic
coercion. In
Proceedings
of 12th International Workshop on Computational
Semantics
.
Mikolov, T., Chen, K., Corrado, G., & Dean, J.
(2013) Efficient
estimation of word representations in vector
space. In
Proceedings
of ICLR
Workshop
.
Miyazawa, A., & Miyao, Y.
(2017) Evaluation
metrics for automatically generated metaphorical
expressions. In
12th
International Workshop on Computational
Semantics
.
Pantel, P.
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L.
(2018) Deep
contextualized word
representations. In
Proceedings
of the 2018 Conference of the North American Chapter of
the Association for Computational
Linguistics
(pp. 2227–2237).
Salton, G., Wong, A., & Yang, C. S.
(1975) A
vector space model for automatic
indexing. In
Proceedings of the 12th ACM
SIGIR
Conference
(pp. 137–150).
Schütze, H.
(1992) Dimensions
of
meaning. In
Proceedings
of the 1992 ACM/IEEE conference on
Supercomputing
(pp. 787–796).
Searle, J. R.
Shutova, E.
Shutova, E., Teufel, S., & Korhonen, A.
Steen, G. J., Dorst, A. G., Herrmann, J. B., Kaal, A., Krennmayr, T., & Pasma, T.
Stickles, E., David, O., Dodge, E. K., & Hong, J.
Sweetser, E.
Tsvetkov, Y., Boytsov, L., Gershman, A., Nyberg, E., & Dyer, C.
van Genabith, J.
(1999) Metaphors
and type
theory. In
Proceedings
of the AISB’99 Symposium on Metaphor, Artificial
Intelligence, and
Cognition
(pp. 108–112).
Veale, T., & Hao, Y.
(2007) Comprehending
and generating apt metaphors: A web-driven, case-based
approach to figurative
language.
AAAI
, 1471–1476.