Metaphorical expressions are pervasive in natural language and pose a substantial challenge for computational semantics. The inherent compositionality of metaphor makes it an important test case for compositional distributional semantic models (CDSMs). This paper is the first to investigate whether metaphorical composition warrants a distinct treatment in the CDSM framework. We propose a method to learn metaphors as linear transformations in a vector space and find that, across a variety of semantic domains, explicitly modeling metaphor improves the resulting semantic representations. We then use these representations in a metaphor identification task, achieving a high performance of 0.82 in terms of F-score.

We investigate the effectiveness of semantic generalizations/classifications for capturing the regularities of the behavior of verbs in terms of their metaphoricity. Starting from orthographic word unigrams, we experiment with various ways of defining semantic classes for verbs (grammatical, resource-based, distributional) and measure the effectiveness of these classes for classifying all verbs in a running text as metaphor or non metaphor.

The diagnosis of serious mental health conditions such as schizophrenia is based on the judgment of clinicians whose training takes many years and cannot be easily formalized into objective measures. However, clinical research suggests there are disturbances in aspects of the language use of patients with schizophrenia, which opens a door for the use of NLP tools in schizophrenia diagnosis and prognosis. Using metaphor-identification and sentiment-analysis algorithms to automatically generate features, we create a classifier that, with high accuracy, can predict which patients will develop (or currently suffer from) schizophrenia. To our knowledge, this study is the first to demonstrate the utility of automated metaphor identification algorithms for detection or prediction of disease.

Arbitrariness of the sign-the notion that the forms of words are unrelated to their meanings-is an underlying assumption of many linguistic theories. Two lines of research have recently challenged this assumption, but they produce differing characterizations of non-arbitrariness in language. Behavioral and corpus studies have confirmed the validity of localized form-meaning patterns manifested in limited subsets of the lexicon. Meanwhile, global (lexicon-wide) statistical analyses instead find diffuse form-meaning systematicity across the lexicon as a whole. We bridge the gap with an approach that can detect both local and global formmeaning systematicity in language. In the kernel regression formulation we introduce, form-meaning relationships can be used to predict words' distributional semantic vectors from their forms. Furthermore, we introduce a novel metric learning algorithm that can learn weighted edit distances that minimize kernel regression error. Our results suggest that the English lexicon exhibits far more global form-meaning systematicity than previously discovered, and that much of this systematicity is focused in localized formmeaning patterns.

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.