Exploring Metaphorical Senses and Word Representations for Identifying Metonyms

A metonym is a word with a figurative meaning, similar to a metaphor. Because metonyms are closely related to metaphors, we apply features that are used successfully for metaphor recognition to the task of detecting metonyms. On the ACL SemEval 2007 Task 8 data with gold standard metonym annotations, our system achieved 86.45% accuracy on the location metonyms. Our code can be found on GitHub.Comment: 9 pages, 8 pages conten

Corpus Analysis and Lexical Pragmatics: An Overview

Lexical pragmatics studies the processes by which lexically encoded meanings are modified in use; well-studied examples include lexical narrowing, approximation and metaphorical extension. Relevance theorists have been trying to develop a unitary account on which narrowing, approximation and metaphorical extension are all explained in the same way. While there have been several corpus-based studies of metaphor and a few of hyperbole or approximation, there has been no attempt so far to test the unitary account using corpus data. This paper reports the results of a corpus-based investigation of lexical-pragmatic processes, and discusses the theoretical issues and challenges it raises

A pragmatic approach to proverb use and interpretation

Proverbs are interesting pieces of popular wisdom and tradition belonging to any culture, which help us to foreground the values and shared beliefs held by a speech community. However, its study has received little attention up to now. Thus, this dissertation research aims to analyze the functions and uses of proverbs taking examples from English and Spanish them. In order to achieve this goal, we have applied Sperber and Wilson’s Relevant Theory to explain how proverbs allow the speaker to express his/her intention in an implicit way. The findings demonstrate that the main functions of proverbs are criticism, advice and warning. In addition, we have offered an explanation of how their often ironical and metaphorical nature affects proverbs’ understanding. Besides, we have studied the use of the ellipsis in proverbs, which takes place in familiar proverbs, analyzing how familiarity and unfamiliarity influences on proverb use. Finally, we summed up our conclusions to achieve a better comprehension of proverbs’ functions and usesLos proverbios son ejemplos de sabiduría popular y tradición de cualquier cultura que nos ayudan a descubrir los valores y creencias compartidas por una comunidad de hablantes. Sin embargo, este estudio ha recibido poca atención hasta ahora. Por lo tanto, el propósito del presente trabajo es describir las funciones y usos de los proverbios y refranes concentrándonos en refranes de la lengua inglés y española para analizar y explicar las funciones y usos de los proverbios en general. Para conseguir nuestro propósito hemos aplicado la Teoría de la Relevancia de Sperber y Wilson a este estudio para explicar como ellos permiten al hablante expresar su intención de forma implícita. Las conclusiones demostrarán que la mayor función de los proverbios es la crítica, el consejo y advertencia. Además, hemos ofrecido una explicación de cómo su frecuente naturaleza irónica y metafórica influye en la comprensión de los proverbios. También hemos estudiado el uso de la elipsis en los proverbios, que toma lugar en los proverbios familiares, analizando como la familiaridad o el desconocimiento influye en el uso de los proverbios. Finalmente, resumiremos nuestras conclusiones para llegar a una mejor comprensión de las funciones y usos de los proverbio

The Latent Relation Mapping Engine: Algorithm and Experiments

Many AI researchers and cognitive scientists have argued that analogy is the core of cognition. The most influential work on computational modeling of analogy-making is Structure Mapping Theory (SMT) and its implementation in the Structure Mapping Engine (SME). A limitation of SME is the requirement for complex hand-coded representations. We introduce the Latent Relation Mapping Engine (LRME), which combines ideas from SME and Latent Relational Analysis (LRA) in order to remove the requirement for hand-coded representations. LRME builds analogical mappings between lists of words, using a large corpus of raw text to automatically discover the semantic relations among the words. We evaluate LRME on a set of twenty analogical mapping problems, ten based on scientific analogies and ten based on common metaphors. LRME achieves human-level performance on the twenty problems. We compare LRME with a variety of alternative approaches and find that they are not able to reach the same level of performance.Comment: related work available at http://purl.org/peter.turney

Predicting Human Metaphor Paraphrase Judgments with Deep Neural Networks

We propose a new annotated corpus for metaphor interpretation by paraphrase, and a novel DNN model for performing this task. Our corpus consists of 200 sets of 5 sen- tences, with each set containing one reference metaphorical sentence, and four ranked candi- date paraphrases. Our model is trained for a binary classification of paraphrase candidates, and then used to predict graded paraphrase ac- ceptability. It reaches an encouraging 75% ac- curacy on the binary classification task, and high Pearson (.75) and Spearman (.68) correla- tions on the gradient judgment prediction task

Multimodal Grounding for Language Processing

This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language. We categorize the information flow in multimodal processing with respect to cognitive models of human information processing and analyze different methods for combining multimodal representations. Based on this methodological inventory, we discuss the benefit of multimodal grounding for a variety of language processing tasks and the challenges that arise. We particularly focus on multimodal grounding of verbs which play a crucial role for the compositional power of language.Comment: The paper has been published in the Proceedings of the 27 Conference of Computational Linguistics. Please refer to this version for citations: https://www.aclweb.org/anthology/papers/C/C18/C18-1197

On the Impact of Temporal Representations on Metaphor Detection

State-of-the-art approaches for metaphor detection compare their literal - or core - meaning and their contextual meaning using metaphor classifiers based on neural networks. However, metaphorical expressions evolve over time due to various reasons, such as cultural and societal impact. Metaphorical expressions are known to co-evolve with language and literal word meanings, and even drive, to some extent, this evolution. This poses the question of whether different, possibly time-specific, representations of literal meanings may impact the metaphor detection task. To the best of our knowledge, this is the first study that examines the metaphor detection task with a detailed exploratory analysis where different temporal and static word embeddings are used to account for different representations of literal meanings. Our experimental analysis is based on three popular benchmarks used for metaphor detection and word embeddings extracted from different corpora and temporally aligned using different state-of-the-art approaches. The results suggest that the usage of different static word embedding methods does impact the metaphor detection task and some temporal word embeddings slightly outperform static methods. However, the results also suggest that temporal word embeddings may provide representations of the core meaning of the metaphor even too close to their contextual meaning, thus confusing the classifier. Overall, the interaction between temporal language evolution and metaphor detection appears tiny in the benchmark datasets used in our experiments. This suggests that future work for the computational analysis of this important linguistic phenomenon should first start by creating a new dataset where this interaction is better represented.Comment: 12 pages, 4 figure

Lexical typology : a programmatic sketch

The present paper is an attempt to lay the foundation for Lexical Typology as a new kind of linguistic typology.1 The goal of Lexical Typology is to investigate crosslinguistically significant patterns of interaction between lexicon and grammar