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008			220601s2019 sz | s |||| 0|eng d
020			_a9783031021695
024	7		_a10.1007/978-3-031-02169-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.D343 _b2019 EB
100	1		_aStede, Manfred, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686329 _d1965-
245	1	0	_aArgumentation Mining _cby Manfred Stede, Jodi Schneider
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XV, 175 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Human Language Technologies _x1947-4059
505	0		_aPreface -- Acknowledgments -- Introduction -- Argumentative Language -- Modeling Arguments -- Corpus Annotation -- Finding Claims -- Finding Supporting and Objecting Statements -- Deriving the Structure of Argumentation -- Assessing Argumentation -- Generating Argumentative Text -- Summary and Perspectives -- Bibliography -- Authors' Biographies -- Index.
520			_aArgumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major conferences and journals of the field. Its goals are to identify argumentation in text or dialogue; to construct representations of the constellation of claims, supporting and attacking moves (in different levels of detail); and to characterize the patterns of reasoning that appear to license the argumentation. Furthermore, recent work also addresses the difficult tasks of evaluating the persuasiveness and quality of arguments. Some of the linguistic genres that are being studied include legal text, student essays, political discourse and debate, newspaper editorials, scientific writing, and others. The book starts with a discussion of the linguistic perspective, characteristics of argumentative language, and their relationship to certain other notions such as subjectivity. Besides the connection to linguistics, argumentation has for a long time been a topic in Artificial Intelligence, where the focus is on devising adequate representations and reasoning formalisms that capture the properties of argumentative exchange. It is generally very difficult to connect the two realms of reasoning and text analysis, but we are convinced that it should be attempted in the long term, and therefore we also touch upon some fundamentals of reasoning approaches. Then the book turns to its focus, the computational side of mining argumentation in text. We first introduce a number of annotated corpora that have been used in the research. From the NLP perspective, argumentation mining shares subtasks with research fields such as subjectivity and sentiment analysis, semantic relation extraction, and discourse parsing. Therefore, many technical approaches are being borrowed from those (and other) fields. We break argumentation mining into a series of subtasks, starting with the preparatory steps of classifying text as argumentative (or not) and segmenting it into elementary units. Then, central steps are the automatic identification of claims, and finding statements that support or oppose the claim. For certain applications, it is also of interest to compute a full structure of an argumentative constellation of statements. Next, we discuss a few steps that try to 'dig deeper': to infer the underlying reasoning pattern for a textual argument, to reconstruct unstated premises (so-called 'enthymemes'), and to evaluate the quality of the argumentation. We also take a brief look at 'the other side' of mining, i.e., the generation or synthesis of argumentative text. The book finishes with a summary of the argumentation mining tasks, a sketch of potential applications, and a--necessarily subjective--outlook for the field.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _9139573 _aLógica _xProceso de datos
650		7	_2embne _9158738 _aProceso en lenguaje natural (Informática)
650		7	_2embne _9162648 _aData mining
700	1		_aSchneider, Jodi _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687990
776	0	8	_iPrinted edition: _z9783031001802
776	0	8	_iPrinted edition: _z9783031010415
776	0	8	_iPrinted edition: _z9783031032974
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02169-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _esc _zSI