000			03922nam a22003735i 4500
001			102640
003			DE-He213
005			20240111050141.0
007			cr nn 008mamaa
008			180316s2018 si | s |||| 0|eng d
020			_a9789811083969
024	7		_a10.1007/978-981-10-8396-9 _2doi
040			_aES-MaUEC _bspa
050		4	_aQ342 _b2018 EB
100	1		_aJoshi, Aditya _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aInvestigations in Computational Sarcasm _cby Aditya Joshi, Pushpak Bhattacharyya, Mark J. Carman.
264		1	_aSingapore _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XII, 143 páginas 12 ilustraciones, 4 ilustraciones a color)
347			_atext file _bPDF
490	0		_aCognitive Systems Monographs _x1867-4925 _v37
505	0		_a1. Introduction -- 2. Literature Survey -- 3. Understanding the Phenomenon of Sarcasm -- 4. Sarcasm Detection using Incongruity within Target Text -- 5. Sarcasm Detection using Contextual Incongruity -- 6. Sarcasm Generation -- 7. Conclusion & Future Work.
520	3		_aThis book describes the authors' investigations of computational sarcasm based on the notion of incongruity. In addition, it provides a holistic view of past work in computational sarcasm and the challenges and opportunities that lie ahead. Sarcastic text is a peculiar form of sentiment expression and computational sarcasm refers to computational techniques that process sarcastic text. To first understand the phenomenon of sarcasm, three studies are conducted: (a) how is sarcasm annotation impacted when done by non-native annotators? (b) How is sarcasm annotation impacted when the task is to distinguish between sarcasm and irony? And (c) can targets of sarcasm be identified by humans and computers. Following these studies, the book proposes approaches for two research problems: sarcasm detection and sarcasm generation. To detect sarcasm, incongruity is captured in two ways: 'intra-textual incongruity' where the authors look at incongruity within the text to be classified (i.e., target text) and 'context incongruity' where the authors incorporate information outside the target text. These approaches use machine-learning techniques such as classifiers, topic models, sequence labelling, and word embeddings. These approaches operate at multiple levels: (a) sentiment incongruity (based on sentiment mixtures), (b) semantic incongruity (based on word embedding distance), (c) language model incongruity (based on unexpected language model), (d) author's historical context (based on past text by the author), and (e) conversational context (based on cues from the conversation). In the second part of the book, the authors present the first known technique for sarcasm generation, which uses a template-based approach to generate a sarcastic response to user input. This book will prove to be a valuable resource for researchers working on sentiment analysis, especially as applied to automation in social media.
650		7	_9666321 _aIngeniería asistida por ordenador
650		7	_9666075 _aLingüística computacional
650		7	_aInteligencia artificial _2embne _9413115
700	1		_aBhattacharyya, Pushpak _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/n2014047844 _1http://viaf.org/viaf/5880153653253655900001/
700	1		_aCarman, Mark J _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/90718735/
776	0	8	_iEdición impresa: _z9789811083952
776	0	8	_iEdición impresa: _z9789811083976
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-10-8396-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aEngineering (Springer-11647)
988			_aEBSPRINGER_2018
998			_b12/2018 _dz _ea _feng _ggw _h0
999			_c102640 _d102640 _x1