Relevance Detection and Argumentation Mining in Medical Domain

ABSTRACT In this paper we describe a method to determine the relevancy of a query with a sentence in the document in the field of medical domain. We also describe a method to determine if the given statement supports the query, opposes the query or is neutral with respect to the query. This is a part of CHIS shared task at FIRE 2016

Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

Never Retreat, Never Retract: Argumentation Analysis for Political Speeches

International audienceIn this work, we apply argumentation mining techniques, in particular relation prediction, to study political speeches in monological form, where there is no direct interaction between opponents. We argue that this kind of technique can effectively support researchers in history, social and political sciences, which must deal with an increasing amount of data in digital form and need ways to automatically extract and analyse argumentation patterns. We test and discuss our approach based on the analysis of documents issued by R. Nixon and J. F. Kennedy during 1960 presidential campaign. We rely on a supervised classifier to predict argument relations (i.e., support and attack), obtaining an accuracy of 0.72 on a dataset of 1,462 argument pairs. The application of argument mining to such data allows not only to highlight the main points of agreement and disagreement between the candidates' arguments over the campaign issues such as Cuba, disarmament and health-care, but also an in-depth argumentative analysis of the respective viewpoints on these topics

HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine

Providing high quality explanations for AI predictions based on machine learning is a challenging and complex task. To work well it requires, among other factors: selecting a proper level of generality/specificity of the explanation; considering assumptions about the familiarity of the explanation beneficiary with the AI task under consideration; referring to specific elements that have contributed to the decision; making use of additional knowledge (e.g. expert evidence) which might not be part of the prediction process; and providing evidence supporting negative hypothesis. Finally, the system needs to formulate the explanation in a clearly interpretable, and possibly convincing, way. Given these considerations, ANTIDOTE fosters an integrated vision of explainable AI, where low-level characteristics of the deep learning process are combined with higher level schemes proper of the human argumentation capacity. ANTIDOTE will exploit cross-disciplinary competences in deep learning and argumentation to support a broader and innovative view of explainable AI, where the need for high-quality explanations for clinical cases deliberation is critical. As a first result of the project, we publish the Antidote CasiMedicos dataset to facilitate research on explainable AI in general, and argumentation in the medical domain in particular.Comment: To appear: In SEPLN 2023: 39th International Conference of the Spanish Society for Natural Language Processin

Computational Argumentation for the Automatic Analysis of Argumentative Discourse and Human Persuasion

Tesis por compendio[ES] La argumentación computacional es el área de investigación que estudia y analiza el uso de distintas técnicas y algoritmos que aproximan el razonamiento argumentativo humano desde un punto de vista computacional. En esta tesis doctoral se estudia el uso de distintas técnicas propuestas bajo el marco de la argumentación computacional para realizar un análisis automático del discurso argumentativo, y para desarrollar técnicas de persuasión computacional basadas en argumentos. Con estos objetivos, en primer lugar se presenta una completa revisión del estado del arte y se propone una clasificación de los trabajos existentes en el área de la argumentación computacional. Esta revisión nos permite contextualizar y entender la investigación previa de forma más clara desde la perspectiva humana del razonamiento argumentativo, así como identificar las principales limitaciones y futuras tendencias de la investigación realizada en argumentación computacional. En segundo lugar, con el objetivo de solucionar algunas de estas limitaciones, se ha creado y descrito un nuevo conjunto de datos que permite abordar nuevos retos y investigar problemas previamente inabordables (e.g., evaluación automática de debates orales). Conjuntamente con estos datos, se propone un nuevo sistema para la extracción automática de argumentos y se realiza el análisis comparativo de distintas técnicas para esta misma tarea. Además, se propone un nuevo algoritmo para la evaluación automática de debates argumentativos y se prueba con debates humanos reales. Finalmente, en tercer lugar se presentan una serie de estudios y propuestas para mejorar la capacidad persuasiva de sistemas de argumentación computacionales en la interacción con usuarios humanos. De esta forma, en esta tesis se presentan avances en cada una de las partes principales del proceso de argumentación computacional (i.e., extracción automática de argumentos, representación del conocimiento y razonamiento basados en argumentos, e interacción humano-computador basada en argumentos), así como se proponen algunos de los cimientos esenciales para el análisis automático completo de discursos argumentativos en lenguaje natural.[CA] L'argumentació computacional és l'àrea de recerca que estudia i analitza l'ús de distintes tècniques i algoritmes que aproximen el raonament argumentatiu humà des d'un punt de vista computacional. En aquesta tesi doctoral s'estudia l'ús de distintes tècniques proposades sota el marc de l'argumentació computacional per a realitzar una anàlisi automàtic del discurs argumentatiu, i per a desenvolupar tècniques de persuasió computacional basades en arguments. Amb aquestos objectius, en primer lloc es presenta una completa revisió de l'estat de l'art i es proposa una classificació dels treballs existents en l'àrea de l'argumentació computacional. Aquesta revisió permet contextualitzar i entendre la investigació previa de forma més clara des de la perspectiva humana del raonament argumentatiu, així com identificar les principals limitacions i futures tendències de la investigació realitzada en argumentació computacional. En segon lloc, amb l'objectiu de sol$\cdot$lucionar algunes d'aquestes limitacions, hem creat i descrit un nou conjunt de dades que ens permet abordar nous reptes i investigar problemes prèviament inabordables (e.g., avaluació automàtica de debats orals). Conjuntament amb aquestes dades, es proposa un nou sistema per a l'extracció d'arguments i es realitza l'anàlisi comparativa de distintes tècniques per a aquesta mateixa tasca. A més a més, es proposa un nou algoritme per a l'avaluació automàtica de debats argumentatius i es prova amb debats humans reals. Finalment, en tercer lloc es presenten una sèrie d'estudis i propostes per a millorar la capacitat persuasiva de sistemes d'argumentació computacionals en la interacció amb usuaris humans. D'aquesta forma, en aquesta tesi es presenten avanços en cada una de les parts principals del procés d'argumentació computacional (i.e., l'extracció automàtica d'arguments, la representació del coneixement i raonament basats en arguments, i la interacció humà-computador basada en arguments), així com es proposen alguns dels fonaments essencials per a l'anàlisi automàtica completa de discursos argumentatius en llenguatge natural.[EN] Computational argumentation is the area of research that studies and analyses the use of different techniques and algorithms that approximate human argumentative reasoning from a computational viewpoint. In this doctoral thesis we study the use of different techniques proposed under the framework of computational argumentation to perform an automatic analysis of argumentative discourse, and to develop argument-based computational persuasion techniques. With these objectives in mind, we first present a complete review of the state of the art and propose a classification of existing works in the area of computational argumentation. This review allows us to contextualise and understand the previous research more clearly from the human perspective of argumentative reasoning, and to identify the main limitations and future trends of the research done in computational argumentation. Secondly, to overcome some of these limitations, we create and describe a new corpus that allows us to address new challenges and investigate on previously unexplored problems (e.g., automatic evaluation of spoken debates). In conjunction with this data, a new system for argument mining is proposed and a comparative analysis of different techniques for this same task is carried out. In addition, we propose a new algorithm for the automatic evaluation of argumentative debates and we evaluate it with real human debates. Thirdly, a series of studies and proposals are presented to improve the persuasiveness of computational argumentation systems in the interaction with human users. In this way, this thesis presents advances in each of the main parts of the computational argumentation process (i.e., argument mining, argument-based knowledge representation and reasoning, and argument-based human-computer interaction), and proposes some of the essential foundations for the complete automatic analysis of natural language argumentative discourses.This thesis has been partially supported by the Generalitat Valenciana project PROME- TEO/2018/002 and by the Spanish Government projects TIN2017-89156-R and PID2020- 113416RB-I00.Ruiz Dolz, R. (2023). Computational Argumentation for the Automatic Analysis of Argumentative Discourse and Human Persuasion [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/194806Compendi

Integrating natural language processing and pragmatic argumentation theories for argumentation support

Natural language processing (NLP) research and design that aims to model and detect opposition in text for the purpose of opinion classification, sentiment analysis, and meeting tracking, generally excludes the interactional, pragmatic aspects of online text. We propose that a promising direction for NLP is to incorporate the insights of pragmatic, dialectical theories of argumentation to more fully exploit the potential of NLP to offer sound, robust systems for various kinds of argumentation support

A literature review. Introduction to the special issue

UIDB/00183/2020 UIDP/00183/2020 PTDC/FER-FIL/28278/2017 CHIST-ERA/0002/2019Argumentation schemes [35, 80, 91] are a relatively recent notion that continues an extremely ancient debate on one of the foundations of human reasoning, human comprehension, and obviously human argumentation, i.e., the topics. To understand the revolutionary nature of Walton’s work on this subject matter, it is necessary to place it in the debate that it continues and contributes to, namely a view of logic that is much broader than the formalistic perspective that has been adopted from the 20th century until nowadays. With his book Argumentation schemes for presumptive reasoning, Walton attempted to start a dialogue between three different fields or views on human reasoning – one (argumentation theory) very recent, one (dialectics) very ancient and with a very long tradition, and one (formal logic) relatively recent, but dominating in philosophy. Argumentation schemes were proposed as dialectical instruments, in the sense that they represented arguments not only as formal relations, but also as pragmatic inferences, as they at the same time depend on what the interlocutors share and accept in a given dialogical circumstance, and affect their dialogical relation. In this introduction, the notion of argumentation scheme will be analyzed in detail, showing its different dimensions and its defining features which make them an extremely useful instrument in Artificial Intelligence. This theoretical background will be followed by a literature review on the uses of the schemes in computing, aimed at identifying the most important areas and trends, the most promising proposals, and the directions of future research.publishersversionpublishe