Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163233
Title: MetaPro: a computational metaphor processing model for text pre-processing
Authors: Mao, Rui
Li, Xiao
Ge, Mengshi
Cambria, Erik
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Mao, R., Li, X., Ge, M. & Cambria, E. (2022). MetaPro: a computational metaphor processing model for text pre-processing. Information Fusion, 86-87, 30-43. https://dx.doi.org/10.1016/j.inffus.2022.06.002
Project: I1901E0046
Journal: Information Fusion
Abstract: Metaphor is a special linguistic phenomenon, challenging diverse natural language processing tasks. Previous works focused on either metaphor identification or domain-specific metaphor interpretation, e.g., interpreting metaphors with a specific part-of-speech, metaphors in a specific application scenario or metaphors with specific concepts. These methods cannot be used directly in everyday texts. In this paper, we propose a metaphor processing model, termed MetaPro, which integrates metaphor identification and interpretation modules for text pre-processing. To the best of our knowledge, this is the first end-to-end metaphor processing approach in the present field. MetaPro can identify metaphors in a sentence on token-level, paraphrasing the identified metaphors into their literal counterparts, and explaining metaphoric multi-word expressions. It achieves state-of-the-art performance in the evaluation of sub-tasks. Besides, the model can be used as a text pre-processing method to support downstream tasks. We examine the utility of MetaPro text pre-processing on a news headline sentiment analysis task. The experimental results show that the performance of sentiment analysis classifiers can be improved with the pre-processed texts.
URI: https://hdl.handle.net/10356/163233
ISSN: 1566-2535
DOI: 10.1016/j.inffus.2022.06.002
Rights: © 2022 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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