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.
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.
Research Areas
