Almela, Ángela

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Almela, Ángela

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Almela, Ángela

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Universidad de Murcia. Departamento de Filología Inglesa

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Open Access
Analysis of context-dependent errors in the medical domain in Spanish: a corpus-based study
(SAGE Publications, 2023-01-18) López Hernández, Jésica; Molina Molina, Fernando; Almela, Ángela; Filología Inglesa
This corpus-based study aimed to investigate the presence of context-dependent linguistic errors in a corpus of clinical reports. The data were taken from a corpus comprising more than 2 million words and made up of clinical reports from emergency medicine, intensive care unit, general surgery, and psychiatry. Quantitative and qualitative analyses were carried out. A language model based on n-grams was developed for the detection of errors, parameters for the selection of cases were defined, and a classification tool was implemented. The findings indicated that emergency medicine was the medical specialty with the highest number of context-dependent errors and that the most frequent type of error was omission of written accent. Furthermore, the analysis revealed the presence of errors of competence due to the incorrect application of the linguistic norm of Spanish, phenomena of phonetic similarity, and composition of words; it is also worth noting that performance errors occurred due to rapid typing on the keyboard. This study constituted the first analysis and creation of a typology of context-dependent errors for the medical domain in Spanish. It contributed to the design of a module based on linguistic knowledge that can be used for the development and improvement of automatic correction systems that, in turn, are used for data processing in medicine.
Open Access
UMUCorpusClassifier: compilation and evaluation of linguistic corpus for Natural Language Processing tasks
(Sociedad Española de Procesamiento del Lenguaje Natural, 2020) Almela, Ángela; García Díaz, José Antonio; Alcaraz Marmol, Gema; Valencia García, Rafael; Filología Inglesa
The development of an annotated corpus is a very time-consuming task. Although some researchers have proposed the automatic annotation of a corpus based on ad-hoc heuristics, valid hypotheses cannot always be made. Even when the annotation process is performed by human annotators, the quality of the corpus is heavily in uenced by disagreements between annotators or with themselves. Therefore, the lack of supervision of the annotation process can lead to poor quality corpus. In this work, we propose a demonstration of UMUCorpusClassi er, a NLP tool for aid researches for compiling corpus as well as coordinating and supervising the annotation process. This tool eases the daily supervision process and permits to detect deviations and inconsistencies during early stages of the annotation process.
Open Access
Seeing through deception: a computational approach to deceit detection in written communication
(Association for Computational Linguistics, 2012) Almela, Ángela; Valencia García, Rafael; Cantos Gómez, Pascual; Filología Inglesa; Informática y Sistemas
The present paper addresses the question of the nature of deception language. Specifically, the main aim of this piece of research is the exploration of deceit in Spanish written communication. We have designed an automatic classifier based on Support Vector Machines (SVM) for the identification of deception in an ad hoc opinion corpus. In order to test the effectiveness of the LIWC2001 categories in Spanish, we have drawn a comparison with a Bag-of-Words (BoW) model. The results indicate that the classification of the texts is more successful by means of our initial set of variables than with the latter system. These findings are potentially applicable to areas such as forensic linguistics and opinion mining, where extensive research on languages other than English isneeded.
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Exploring verbal cues to deception: testing quantitative linguistic methods on English and Spanish
(Peter Lang, 2023) Almela, Ángela; Filología Inglesa; Facultades de la UMU::Facultad de Letras
In this research monograph, two empirical studies are presented, whose aim is to explore the linguistic cues to deception in written English and Spanish using computational tools like ALIAS WISER and LIWC. The tools have been tested on ground-truth data. After the automated text analysis, statistical classifiers are used to determine the best protocol for computational classification of true and false statements, and the role of emotional involvement is analyzed in low-stakes deception. The results demonstrate that, in our corpora, there is a real difference between "laboratory-produced" lies told in an experimental setting and high-stakes lies told in a police investigation.
Open Access
Detección automática de errores lingüísticos en textos clínicos: análisis de patrones de error en varias especialidades médicas
(Tremédica, 2021) López Hernández, Jésica; Almela, Ángela; Lengua Española, Lingüistica General; Filología Inglesa; Facultades de la UMU::Facultad de Letras
El objetivo de este trabajo es aportar el primer análisis cuantitativo de tipos de errores contenidos en un corpus formado por informes clínicos en español. Se han analizado informes clínicos pertenecientes a las especialidades de urgencias, uci, psiquiatría y cirugía general. Los errores fueron estudiados teniendo en cuenta criterios como distancia de edición, tipo de error o existencia de multierror en la palabra. Para tal cometido, se desarrolló una herramienta de identificación y clasificación de errores, se utilizaron técnicas estadísticas y se compararon los resultados con trabajos previos sobre patrones de errores. Los resultados indican que el tipo de error más frecuente es el de omisión de tilde y la mayoría de los errores ocurren a distancia de edición 1, entre parejas de caracteres con similitudes fonéticas y parejas de caracteres adyacentes en el teclado.
Open Access
A study on LIWC categories for opinion mining in Spanish reviews
(SAGE Publications, 2014-08-26) Salas Zárate, María del Pilar; López López, Estanislao; Valencia García, Rafael; Aussenac Gilles, Natalie; Almela, Ángela; Alor Hernández, Giner; Filología Inglesa
With the exponential growth of social media, that is, blogs and social networks, organizations and individual persons are increasingly using the number of reviews of these media for decision-making about a product or service. Opinion mining detects whether the emotions of an opinion expressed by a user on Web platforms in natural language are positive or negative. This paper presents extensive experiments to study the effectiveness of the classification of Spanish opinions in five categories: highly positive, highly negative, positive, negative and neutral, using the combination of the psychological and linguistic features of LIWC (Linguistic Inquiry and Word Count). LIWC is a text analysis software that enables the extraction of different psychological and linguistic features from natural language text. For this study, two corpora have been used, one about movies and one about technological products. Furthermore, we conducted a comparative assessment of the performance of various classification techniques, J48, SMO and BayesNet, using precision, recall and F-measure metrics. The findings revealed that the positive and negative categories provide better results than the other categories. Finally, experiments on both corpora indicated that SMO produces better results than BayesNet and J48 algorithms, obtaining an F-measure of 90.4 and 87.2% in each domain.
Open Access
Overview of FinancES 2023: Financial Targeted Sentiment Analysis in Spanish
(Sociedad Española de Procesamiento del Lenguaje Natural, 2023-09) Almela, Ángela; García Díaz, José Antonio; García Sánchez, Francisco; Alcaraz Mármol, Gema; Marín Pérez, María José; Valencia García, Rafael; Filología Inglesa
This paper presents the FinancES 2023 shared task, organized in the IberLEF 2023 workshop, within the framework of the 39th International Conference of the Spanish Society for Natural Language Processing (SEPLN 2023). The aim of this task is to extend the challenge of sentiment analysis in Spanish to the financial domain, in order to extract the sentiment that a piece of financial information can have for several actors, including the main economic target (i.e., the specific company or asset where the economic fact applies), other companies (i.e., the entities producing the goods and services that others consume) and consumers (i.e., households/individuals). Specifically, two tasks are proposed and evaluated separately. One to identify the main target and to determine the sentiment polarity towards such target, and a second task to assess the sentiment towards both other companies and consumers. The ranking includes results for 10 different teams proposing novel approaches, mostly based on Transformers and generative language models.
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Automatic spelling detection and correction in the medical domain: a systematic literature review
(Springer Nature, 2019) López Hernández, Jésica; Almela, Ángela; Valencia García, Rafael; Filología Inglesa
Automatic spelling correction is one of the most important problems in natural language processing. Its difficulty increases in medical corpora, due to the intrinsic particularities that have these texts. These features include the use of specific terminology, abbreviations, acronyms and the presence of writing errors. In this article we present a systematic review of the literature on automatic spelling detection and correction for the medical domain. There are many works on detection and automatic correction, but there is no review delving into the process of automatic correction in the medical domain. Therefore, we intend to synthesize all the existing information on this research topic and the types of studies that have been carried out to date. We present the main techniques and resources, and finally also the limitations and specific challenges. The results reflect the importance of compiling an exhaustive dictionary. In addition, the results show the ordinary use of distance algorithms of spelling and phonetic similarity, as well as with statistical techniques. The improvement of performance in recent years is especially relevant because of the use of context-based methods, such as linguistic models or neural embeddings.
Open Access
Corpus Applications in Forensic Computational Linguistics - LAEL Webinar
(2020-09-22) Almela, Ángela; Filología Inglesa
Open Access
Quantitative Methods Research in Language and Linguistics & SPSS: An introduction
Almela, Ángela; Facultad de Letras