Métodos de aprendizaje profundo para la extracción de nombres metafóricos de flores y plantas

The domain of Botany is rich with metaphorical terms. Those terms play an important role in the description and identification of flowers and plants. However, the identification of such terms in discourse is an arduous task. This leads in some cases to committing errors during translation processes and lexicographic tasks. The process is even more challenging when it comes to machine translation, both in the cases of single-word terms and multi-word terms. One of the recent concerns of Natural Language Processing (NLP) applications and Machine Translation (MT) technologies is the automatic identification of metaphor-based words in discourse through Deep Learning (DL). In this study, we seek to fill this gap through the use of thirteen popular transformer based models, as well as ChatGPT, and we show that discriminative models perform better than GPT-3.5 model with our best performer reporting 92.2349% F1 score in metaphoric flower and plant names identification task.El dominio de la Botánica es rico en términos metafóricos. Estos términos tienen un papel importante en la descripción e identificación de flores y plantas. Sin embargo, la identificación de este tipo de términos en el discurso es una tarea difícil. Esto puede conducir a errores en los procesos de traducción y otras tareas lexicográficas. Este proceso es aún más difícil cuando se trata de traducción automática, tanto en el caso de las unidades monoléxicas, como en el caso de las unidades multiléxicas. Uno de los desafíos a los que se enfrentan las aplicaciones del Procesamiento del Lenguaje Natural y las tecnologías de Traducción Automática es la identificación de términos basados en metáfora a través de métodos de aprendizaje profundo. En este estudio, tenemos el objetivo de rellenar este vacío a través del uso de trece modelos populares basados en transformadores, además del ChatGPT. Asimismo, demostramos que los modelos discriminativos aportan mejores resultados que los modelos de GPT-3.5. El mejor resultado alcanzó una puntuación de 92,2349% F1 en las tareas de identificación de nombres metafóricos de flores y plantas.Part of this research was carried within the framework of the projects the projects PID2020-118369GB-I00 and A-HUM-600-UGR20, funded by the Spanish Ministry of Science and Innovation and the Regional Government of Andalusia. Funding was also provided by an FPU grant (FPU18/05327) given by the Spanish Ministry of Education

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

Deep Learning Methods for Extracting Metaphorical Names of Flowers and Plants

The domain of Botany is rich with metaphoical terms. Those terms play an important role in the description and identification of flowers and plants. However, the identification of such terms in discourse is an arduous task. This leads in some cases to committing errors during translation processes and lexicographic tasks. The process is even more challenging when it comes to machine translation, both in the cases of single-word terms and multi-word terms. One of the recent concerns of Natural Language Processing (NLP) applications and Machine Translation (MT) technologies is the automatic identification of metaphor-based words in discourse through Deep Learning (DL). In this study, we seek to fill this gap through the use of thirteen popular transformer based models, as well as ChatGPT, and we show that discriminative models perform better than GPT-3.5 model with our best performer reporting 92.2349% F1 score in metaphoric flower and plant names identification task

Figurative Language Detection using Deep Learning and Contextual Features

The size of data shared over the Internet today is gigantic. A big bulk of it comes from postings on social networking sites such as Twitter and Facebook. Some of it also comes from online news sites such as CNN and The Onion. This type of data is very good for data analysis since they are very personalized and specific. For years, researchers in academia and various industries have been analyzing this type of data. The purpose includes product marketing, event monitoring, and trend analysis. The highest usage for this type of analysis is to find out the sentiments of the public about a certain topic or product. This field is called sentiment analysis. The writers of such posts have no obligation to stick to only literal language. They also have the freedom to use figurative language in their publications. Hence, online posts can be categorized into two: Literal and Figurative. Literal posts contain words or sentences that are direct or straight to the point. On the contrary, figurative posts contain words, phrases, or sentences that carry different meanings than usual. This could flip the whole polarity of a given post. Due to this nature, it can jeopardize sentiment analysis works that focus primarily on the polarity of the posts. This makes figurative language one of the biggest problems in sentiment analysis. Hence, detecting it would be crucial and significant. However, the study of figurative language detection is non-trivial. There have been many existing works that tried to execute the task of detecting figurative language correctly, with different methodologies used. The results are impressive but still can be improved. This thesis offers a new way to solve this problem. There are essentially seven commonly used figurative language categories: sarcasm, metaphor, satire, irony, simile, humor, and hyperbole. This thesis focuses on three categories. The thesis aims to understand the contextual meaning behind the three figurative language categories, using a combination of deep learning architecture with manually extracted features and explore the use of well know machine learning classifiers for the detection tasks. In the process, it also aims to describe a descending list of features according to the importance. The deep learning architecture used in this work is Convolutional Neural Network, which is combined with manually extracted features that are carefully chosen based on the literature and understanding of each figurative language. The findings of this work clearly showed improvement in the evaluation metrics when compared to existing works in the same domain. This happens in all of the figurative language categories, proving the framework’s possession of quality

Computational Modeling of Metaphor in Discourse

 Metaphor is used as a language resource/tool to better represent one’s point in communication. It can help achieving social goals such as illustrating attitudes indirectly. This thesis aims to understand metaphor from this social perspective in order to capture how metaphor is used in a discourse and identify a broad spectrum of predictors from the discourse context that contribute towards its detection. We build computational models for metaphor detection that adopt the notion of framing in discourse, a well-known approach for conceptualizing discourse processes. I claim that developing computational models based on this view paves the way for metaphor processing at the discourse level such as extended metaphor detection, and ultimately contribute to modeling people’s use of metaphor in interaction.  In order to model metaphor from this social perspective, we begin with corpus studies to observe people’s use of metaphor in three distinct domains where people use different metaphors for different purposes. This foundational work reveals how the layperson conception of metaphor differs from the technical operationalization of linguists from past work. The focus of our subsequent work is on metaphorical language that is recognizable as such by laypersons.  Next, we perform two case studies, which illuminate the value of metaphor detection in discourse, to explore situational factors that affect people’s use of metaphor. The first study investigates inner situational factors. We build logistic regression models to discover whether metaphor usage is influenced by three psychological distress conditions including PTSD, depression, and anxiety. Our annotation scheme allows separating effects on language choices of the three factors: contextual expectations, content of the message, and framing. Separating these factors gives us deeper insight into understanding people’s metaphor choice, and necessitates consideration of these factors in our next studies. The second study examines external situational factors. We investigate the influence of stressful cancer events on people’s use of metaphor. This study verifies the association between the cancer events and metaphor usage, and the effectiveness of the situational factor as a new type of predictor for metaphor detection.   Then, we build computational models for detecting metaphors that can be around related metaphors, not restricted in their syntactic positions. These models find topical patterns by leveraging lexical context, to explore how a metaphorical frame switch is distinguished from a literal one. We design, implement, and evaluate computational models of three kinds: (1) features of frame contrast, which capture lexical contrast around metaphorical frames; (2) features of frame transition, which capture topic transition patterns occurring around metaphorical frames; and (3) features of frame facets, which capture frame facet patterns occurring around metaphorical frames. We demonstrate that these three features in a nonlinear machine learning model are effective in metaphor detection, and discuss the mechanism through which the frame information enables more accurate metaphor detection in discourse.</p