Abstract
Recognizing metaphors and identifying the source-target mappings is an important task as metaphorical text poses a big challenge for machine reading. To address this problem, we automatically acquire a metaphor knowledge base and an isA knowledge base from billions of web pages. Using the knowledge bases, we develop an inference mechanism to recognize and explain the metaphors in the text. To our knowledge, this is the first purely data-driven approach of probabilistic metaphor acquisition, recognition, and explanation. Our results shows that it significantly outperforms other state-of-the-art methods in recognizing and explaining metaphors.- Anthology ID:
- Q13-1031
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 1
- Month:
- Year:
- 2013
- Address:
- Cambridge, MA
- Editors:
- Dekang Lin, Michael Collins
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 379–390
- Language:
- URL:
- https://aclanthology.org/Q13-1031
- DOI:
- 10.1162/tacl_a_00235
- Cite (ACL):
- Hongsong Li, Kenny Q. Zhu, and Haixun Wang. 2013. Data-Driven Metaphor Recognition and Explanation. Transactions of the Association for Computational Linguistics, 1:379–390.
- Cite (Informal):
- Data-Driven Metaphor Recognition and Explanation (Li et al., TACL 2013)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/Q13-1031.pdf