Title
Multilingual Metaphor Processing: Experiments with Semi-Supervised and Unsupervised Learning.
Abstract
Highly frequent in language and communication, metaphor represents a significant challenge for Natural Language Processing NLP applications. Computational work on metaphor has traditionally evolved around the use of hand-coded knowledge, making the systems hard to scale. Recent years have witnessed a rise in statistical approaches to metaphor processing. However, these approaches often require extensive human annotation effort and are predominantly evaluated within a limited domain. In contrast, we experiment with weakly supervised and unsupervised techniques-with little or no annotation-to generalize higher-level mechanisms of metaphor from distributional properties of concepts. We investigate different levels and types of supervision learning from linguistic examples vs. learning from a given set of metaphorical mappings vs. learning without annotation in flat and hierarchical, unconstrained and constrained clustering settings. Our aim is to identify the optimal type of supervision for a learning algorithm that discovers patterns of metaphorical association from text. In order to investigate the scalability and adaptability of our models, we applied them to data in three languages from different language groups-English, Spanish, and Russian-achieving state-of-the-art results with little supervision. Finally, we demonstrate that statistical methods can facilitate and scale up cross-linguistic research on metaphor.
Year
DOI
Venue
2017
10.1162/COLI_a_00275
Computational Linguistics
Field
DocType
Volume
Adaptability,Annotation,Computer science,Unsupervised learning,Natural language processing,Constrained clustering,Artificial intelligence,Metaphor,Machine learning,Scalability
Journal
43
Issue
ISSN
Citations 
1
0891-2017
5
PageRank 
References 
Authors
0.49
39
5
Name
Order
Citations
PageRank
Ekaterina Shutova122821.51
Lin Sun217410.14
E. Gutiérrez3203.88
Patricia Lichtenstein450.49
Srini Narayanan51320170.23