Contextual bipolarity and its quality criteria in bipolar linguistic summaries

Bipolar linguistic summaries of data are assumed to be an extension of the ‘classical’ linguistic summarization, a data mining technique revealing complex patterns present in data in a human consistent form. The extension proposal is based on the possibilistic interpretation of the ‘and possibly’ operator and introduced notion of context, which results in the introduction of the new ‘contextual and possibly’ operator. As the end user is expecting the most relevant summaries, ways of determining the quality of summary propositions (quality measures) needs to be developed. Here we focus on specific insights into the quality measures of proposed bipolar linguistic summaries of data and present some basic examples of their correctness and necessity of introduction

JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)

Role of emotion in information retrieval

The main objective of Information Retrieval (IR) systems is to satisfy searchers’ needs. A great deal of research has been conducted in the past to attempt to achieve a better insight into searchers’ needs and the factors that can potentially influence the success of an Information Retrieval and Seeking (IR&S) process. One of the factors which has been considered is searchers’ emotion. It has been shown in previous research that emotion plays an important role in the success of an IR&S process, which has the purpose of satisfying an information need. However, these previous studies do not give a sufficiently prominent position to emotion in IR, since they limit the role of emotion to a secondary factor, by assuming that a lack of knowledge (the need for information) is the primary factor (the motivation of the search). In this thesis, we propose to treat emotion as the principal factor in the system of needs of a searcher, and therefore one that ought to be considered by the retrieval algorithms. We present a more realistic view of searchers’ needs by considering not only theories from information retrieval and science, but also from psychology, philosophy, and sociology. We extensively report on the role of emotion in every aspect of human behaviour, both at an individual and social level. This serves not only to modify the current IR views of emotion, but more importantly to uncover social situations where emotion is the primary factor (i.e., source of motivation) in an IR&S process. We also show that the emotion aspect of documents plays an important part in satisfying the searcher’s need, in particular when emotion is indeed a primary factor. Given the above, we define three concepts, called emotion need, emotion object and emotion relevance, and present a conceptual map that utilises these concepts in IR tasks and scenarios. In order to investigate the practical concepts such as emotion object and emotion relevance in a real-life application, we first study the possibility of extracting emotion from text, since this is the first pragmatic challenge to be solved before any IR task can be tackled. For this purpose, we developed a text-based emotion extraction system and demonstrate that it outperforms other available emotion extraction approaches. Using the developed emotion extraction system, the usefulness of the practical concepts mentioned above is studied in two scenarios: movie recommendation and news diversification. In the movie recommendation scenario, two collaborative filtering (CF) models were proposed. CF systems aim to recommend items to a user, based on the information gathered from other users who have similar interests. CF techniques do not handle data sparsity well, especially in the case of the cold start problem, where there is no past rating for an item. In order to predict the rating of an item for a given user, the first and second models rely on an extension of state-of-the-art memory-based and model-based CF systems. The features used by the models are two emotion spaces extracted from the movie plot summary and the reviews made by users, and three semantic spaces, namely, actor, director, and genre. Experiments with two MovieLens datasets show that the inclusion of emotion information significantly improves the accuracy of prediction when compared with the state-of-the-art CF techniques, and also tackles data sparsity issues. In the news retrieval scenario, a novel way of diversifying results, i.e., diversifying based on the emotion aspect of documents, is proposed. For this purpose, two approaches are introduced to consider emotion features for diversification, and they are empirically tested on the TREC 678 Interactive Track collection. The results show that emotion features are capable of enhancing retrieval effectiveness. Overall, this thesis shows that emotion plays a key role in IR and that its importance needs to be considered. At a more detailed level, it illustrates the crucial part that emotion can play in • searchers, both as a primary (emotion need) and secondary factor (influential role) in an IR&S process; • enhancing the representation of a document using emotion features (emotion object); and finally, • improving the effectiveness of IR systems at satisfying searchers’ needs (emotion relevance)

Benefits and Harms of Large Language Models in Digital Mental Health

The past decade has been transformative for mental health research and practice. The ability to harness large repositories of data, whether from electronic health records (EHR), mobile devices, or social media, has revealed a potential for valuable insights into patient experiences, promising early, proactive interventions, as well as personalized treatment plans. Recent developments in generative artificial intelligence, particularly large language models (LLMs), show promise in leading digital mental health to uncharted territory. Patients are arriving at doctors' appointments with information sourced from chatbots, state-of-the-art LLMs are being incorporated in medical software and EHR systems, and chatbots from an ever-increasing number of startups promise to serve as AI companions, friends, and partners. This article presents contemporary perspectives on the opportunities and risks posed by LLMs in the design, development, and implementation of digital mental health tools. We adopt an ecological framework and draw on the affordances offered by LLMs to discuss four application areas -- care-seeking behaviors from individuals in need of care, community care provision, institutional and medical care provision, and larger care ecologies at the societal level. We engage in a thoughtful consideration of whether and how LLM-based technologies could or should be employed for enhancing mental health. The benefits and harms our article surfaces could serve to help shape future research, advocacy, and regulatory efforts focused on creating more responsible, user-friendly, equitable, and secure LLM-based tools for mental health treatment and intervention

Attention-based Approaches for Text Analytics in Social Media and Automatic Summarization

[ES] Hoy en día, la sociedad tiene acceso y posibilidad de contribuir a grandes cantidades de contenidos presentes en Internet, como redes sociales, periódicos online, foros, blogs o plataformas de contenido multimedia. Todo este tipo de medios han tenido, durante los últimos años, un impacto abrumador en el día a día de individuos y organizaciones, siendo actualmente medios predominantes para compartir, debatir y analizar contenidos online. Por este motivo, resulta de interés trabajar sobre este tipo de plataformas, desde diferentes puntos de vista, bajo el paraguas del Procesamiento del Lenguaje Natural. En esta tesis nos centramos en dos áreas amplias dentro de este campo, aplicadas al análisis de contenido en línea: análisis de texto en redes sociales y resumen automático. En paralelo, las redes neuronales también son un tema central de esta tesis, donde toda la experimentación se ha realizado utilizando enfoques de aprendizaje profundo, principalmente basados en mecanismos de atención. Además, trabajamos mayoritariamente con el idioma español, por ser un idioma poco explorado y de gran interés para los proyectos de investigación en los que participamos. Por un lado, para el análisis de texto en redes sociales, nos enfocamos en tareas de análisis afectivo, incluyendo análisis de sentimientos y detección de emociones, junto con el análisis de la ironía. En este sentido, se presenta un enfoque basado en Transformer Encoders, que consiste en contextualizar \textit{word embeddings} pre-entrenados con tweets en español, para abordar tareas de análisis de sentimiento y detección de ironía. También proponemos el uso de métricas de evaluación como funciones de pérdida, con el fin de entrenar redes neuronales, para reducir el impacto del desequilibrio de clases en tareas \textit{multi-class} y \textit{multi-label} de detección de emociones. Adicionalmente, se presenta una especialización de BERT tanto para el idioma español como para el dominio de Twitter, que tiene en cuenta la coherencia entre tweets en conversaciones de Twitter. El desempeño de todos estos enfoques ha sido probado con diferentes corpus, a partir de varios \textit{benchmarks} de referencia, mostrando resultados muy competitivos en todas las tareas abordadas. Por otro lado, nos centramos en el resumen extractivo de artículos periodísticos y de programas televisivos de debate. Con respecto al resumen de artículos, se presenta un marco teórico para el resumen extractivo, basado en redes jerárquicas siamesas con mecanismos de atención. También presentamos dos instancias de este marco: \textit{Siamese Hierarchical Attention Networks} y \textit{Siamese Hierarchical Transformer Encoders}. Estos sistemas han sido evaluados en los corpora CNN/DailyMail y NewsRoom, obteniendo resultados competitivos en comparación con otros enfoques extractivos coetáneos. Con respecto a los programas de debate, se ha propuesto una tarea que consiste en resumir las intervenciones transcritas de los ponentes, sobre un tema determinado, en el programa "La Noche en 24 Horas". Además, se propone un corpus de artículos periodísticos, recogidos de varios periódicos españoles en línea, con el fin de estudiar la transferibilidad de los enfoques propuestos, entre artículos e intervenciones de los participantes en los debates. Este enfoque muestra mejores resultados que otras técnicas extractivas, junto con una transferibilidad de dominio muy prometedora.[CA] Avui en dia, la societat té accés i possibilitat de contribuir a grans quantitats de continguts presents a Internet, com xarxes socials, diaris online, fòrums, blocs o plataformes de contingut multimèdia. Tot aquest tipus de mitjans han tingut, durant els darrers anys, un impacte aclaparador en el dia a dia d'individus i organitzacions, sent actualment mitjans predominants per compartir, debatre i analitzar continguts en línia. Per aquest motiu, resulta d'interès treballar sobre aquest tipus de plataformes, des de diferents punts de vista, sota el paraigua de l'Processament de el Llenguatge Natural. En aquesta tesi ens centrem en dues àrees àmplies dins d'aquest camp, aplicades a l'anàlisi de contingut en línia: anàlisi de text en xarxes socials i resum automàtic. En paral·lel, les xarxes neuronals també són un tema central d'aquesta tesi, on tota l'experimentació s'ha realitzat utilitzant enfocaments d'aprenentatge profund, principalment basats en mecanismes d'atenció. A més, treballem majoritàriament amb l'idioma espanyol, per ser un idioma poc explorat i de gran interès per als projectes de recerca en els que participem. D'una banda, per a l'anàlisi de text en xarxes socials, ens enfoquem en tasques d'anàlisi afectiu, incloent anàlisi de sentiments i detecció d'emocions, juntament amb l'anàlisi de la ironia. En aquest sentit, es presenta una aproximació basada en Transformer Encoders, que consisteix en contextualitzar \textit{word embeddings} pre-entrenats amb tweets en espanyol, per abordar tasques d'anàlisi de sentiment i detecció d'ironia. També proposem l'ús de mètriques d'avaluació com a funcions de pèrdua, per tal d'entrenar xarxes neuronals, per reduir l'impacte de l'desequilibri de classes en tasques \textit{multi-class} i \textit{multi-label} de detecció d'emocions. Addicionalment, es presenta una especialització de BERT tant per l'idioma espanyol com per al domini de Twitter, que té en compte la coherència entre tweets en converses de Twitter. El comportament de tots aquests enfocaments s'ha provat amb diferents corpus, a partir de diversos \textit{benchmarks} de referència, mostrant resultats molt competitius en totes les tasques abordades. D'altra banda, ens centrem en el resum extractiu d'articles periodístics i de programes televisius de debat. Pel que fa a l'resum d'articles, es presenta un marc teòric per al resum extractiu, basat en xarxes jeràrquiques siameses amb mecanismes d'atenció. També presentem dues instàncies d'aquest marc: \textit{Siamese Hierarchical Attention Networks} i \textit{Siamese Hierarchical Transformer Encoders}. Aquests sistemes s'han avaluat en els corpora CNN/DailyMail i Newsroom, obtenint resultats competitius en comparació amb altres enfocaments extractius coetanis. Pel que fa als programes de debat, s'ha proposat una tasca que consisteix a resumir les intervencions transcrites dels ponents, sobre un tema determinat, al programa "La Noche en 24 Horas". A més, es proposa un corpus d'articles periodístics, recollits de diversos diaris espanyols en línia, per tal d'estudiar la transferibilitat dels enfocaments proposats, entre articles i intervencions dels participants en els debats. Aquesta aproximació mostra millors resultats que altres tècniques extractives, juntament amb una transferibilitat de domini molt prometedora.[EN] Nowadays, society has access, and the possibility to contribute, to large amounts of the content present on the internet, such as social networks, online newspapers, forums, blogs, or multimedia content platforms. These platforms have had, during the last years, an overwhelming impact on the daily life of individuals and organizations, becoming the predominant ways for sharing, discussing, and analyzing online content. Therefore, it is very interesting to work with these platforms, from different points of view, under the umbrella of Natural Language Processing. In this thesis, we focus on two broad areas inside this field, applied to analyze online content: text analytics in social media and automatic summarization. Neural networks are also a central topic in this thesis, where all the experimentation has been performed by using deep learning approaches, mainly based on attention mechanisms. Besides, we mostly work with the Spanish language, due to it is an interesting and underexplored language with a great interest in the research projects we participated in. On the one hand, for text analytics in social media, we focused on affective analysis tasks, including sentiment analysis and emotion detection, along with the analysis of the irony. In this regard, an approach based on Transformer Encoders, based on contextualizing pretrained Spanish word embeddings from Twitter, to address sentiment analysis and irony detection tasks, is presented. We also propose the use of evaluation metrics as loss functions, in order to train neural networks for reducing the impact of the class imbalance in multi-class and multi-label emotion detection tasks. Additionally, a specialization of BERT both for the Spanish language and the Twitter domain, that takes into account inter-sentence coherence in Twitter conversation flows, is presented. The performance of all these approaches has been tested with different corpora, from several reference evaluation benchmarks, showing very competitive results in all the tasks addressed. On the other hand, we focused on extractive summarization of news articles and TV talk shows. Regarding the summarization of news articles, a theoretical framework for extractive summarization, based on siamese hierarchical networks with attention mechanisms, is presented. Also, we present two instantiations of this framework: Siamese Hierarchical Attention Networks and Siamese Hierarchical Transformer Encoders. These systems were evaluated on the CNN/DailyMail and the NewsRoom corpora, obtaining competitive results in comparison to other contemporary extractive approaches. Concerning the TV talk shows, we proposed a text summarization task, for summarizing the transcribed interventions of the speakers, about a given topic, in the Spanish TV talk shows of the ``La Noche en 24 Horas" program. In addition, a corpus of news articles, collected from several Spanish online newspapers, is proposed, in order to study the domain transferability of siamese hierarchical approaches, between news articles and interventions of debate participants. This approach shows better results than other extractive techniques, along with a very promising domain transferability.González Barba, JÁ. (2021). Attention-based Approaches for Text Analytics in Social Media and Automatic Summarization [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/172245TESI

Information fusion for automated question answering

Until recently, research efforts in automated Question Answering (QA) have mainly focused on getting a good understanding of questions to retrieve correct answers. This includes deep parsing, lookups in ontologies, question typing and machine learning of answer patterns appropriate to question forms. In contrast, I have focused on the analysis of the relationships between answer candidates as provided in open domain QA on multiple documents. I argue that such candidates have intrinsic properties, partly regardless of the question, and those properties can be exploited to provide better quality and more user-oriented answers in QA.Information fusion refers to the technique of merging pieces of information from different sources. In QA over free text, it is motivated by the frequency with which different answer candidates are found in different locations, leading to a multiplicity of answers. The reason for such multiplicity is, in part, the massive amount of data used for answering, and also its unstructured and heterogeneous content: Besides am¬ biguities in user questions leading to heterogeneity in extractions, systems have to deal with redundancy, granularity and possible contradictory information. Hence the need for answer candidate comparison. While frequency has proved to be a significant char¬ acteristic of a correct answer, I evaluate the value of other relationships characterizing answer variability and redundancy.Partially inspired by recent developments in multi-document summarization, I re¬ define the concept of "answer" within an engineering approach to QA based on the Model-View-Controller (MVC) pattern of user interface design. An "answer model" is a directed graph in which nodes correspond to entities projected from extractions and edges convey relationships between such nodes. The graph represents the fusion of information contained in the set of extractions. Different views of the answer model can be produced, capturing the fact that the same answer can be expressed and pre¬ sented in various ways: picture, video, sound, written or spoken language, or a formal data structure. Within this framework, an answer is a structured object contained in the model and retrieved by a strategy to build a particular view depending on the end user (or taskj's requirements.I describe shallow techniques to compare entities and enrich the model by discovering four broad categories of relationships between entities in the model: equivalence, inclusion, aggregation and alternative. Quantitatively, answer candidate modeling im¬ proves answer extraction accuracy. It also proves to be more robust to incorrect answer candidates than traditional techniques. Qualitatively, models provide meta-information encoded by relationships that allow shallow reasoning to help organize and generate the final output

Using of Natural Language Processing Techniques in Suicide Research

It is estimated that each year many people, most of whom are teenagers and young adults die by suicide worldwide. Suicide receives special attention with many countries developing national strategies for prevention. Since, more medical information is available in text, Preventing the growing trend of suicide in communities requires analyzing various textual resources, such as patient records, information on the web or questionnaires. For this purpose, this study systematically reviews recent studies related to the use of natural language processing techniques in the area of people’s health who have completed suicide or are at risk. After electronically searching for the PubMed and ScienceDirect databases and studying articles by two reviewers, 21 articles matched the inclusion criteria. This study revealed that, if a suitable data set is available, natural language processing techniques are well suited for various types of suicide related research

Proceedings of the Conference on Natural Language Processing 2010

This book contains state-of-the-art contributions to the 10th conference on Natural Language Processing, KONVENS 2010 (Konferenz zur Verarbeitung natürlicher Sprache), with a focus on semantic processing. The KONVENS in general aims at offering a broad perspective on current research and developments within the interdisciplinary field of natural language processing. The central theme draws specific attention towards addressing linguistic aspects ofmeaning, covering deep as well as shallow approaches to semantic processing. The contributions address both knowledgebased and data-driven methods for modelling and acquiring semantic information, and discuss the role of semantic information in applications of language technology. The articles demonstrate the importance of semantic processing, and present novel and creative approaches to natural language processing in general. Some contributions put their focus on developing and improving NLP systems for tasks like Named Entity Recognition or Word Sense Disambiguation, or focus on semantic knowledge acquisition and exploitation with respect to collaboratively built ressources, or harvesting semantic information in virtual games. Others are set within the context of real-world applications, such as Authoring Aids, Text Summarisation and Information Retrieval. The collection highlights the importance of semantic processing for different areas and applications in Natural Language Processing, and provides the reader with an overview of current research in this field