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020			_a9783030789619
024	7		_a10.1007/978-3-030-78961-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.N38 _b2021 EB
100	1		_aZhong, Xiaoshi _eautor _4aut _9680973
245	1	0	_aTime Expression and Named Entity Recognition _cby Xiaoshi Zhong, Erik Cambria
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
264		4	_c2021
300			_a1 recurso en línea (XIX, 96 páginas) _b17 ilustraciones
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
490	0		_aSocio-Affective Computing _x2509-5714 _v10
490	0		_aBiomedical and Life Sciences (SpringerNature-11642)
490	0		_aBiomedical and Life Sciences (R0) (SpringerNature-43708)
505	0		_aChapter 1. Introduction -- Chapter 2. Literature Review -- Chapter 3. Data Analysis -- Chapter 4. SynTime: Token Types and Heuristic Rules -- 5. TOMN: Constituent-based Tagging Scheme -- Chapter 6. UGTO: Uncommon Words and Proper Nouns -- Chapter 7. Conclusion and Future Work.
520	3		_aThis book presents a synthetic analysis about the characteristics of time expressions and named entities, and some proposed methods for leveraging these characteristics to recognize time expressions and named entities from unstructured text. For modeling these two kinds of entities, the authors propose a rule-based method that introduces an abstracted layer between the specific words and the rules, and two learning-based methods that define a new type of tagging scheme based on the constituents of the entities, different from conventional position-based tagging schemes that cause the problem of inconsistent tag assignment. The authors also find that the length-frequency of entities follows a family of power-law distributions. This finding opens a door, complementary to the rank-frequency of words, to understand our communicative system in terms of language use.
988			_aSpringer_BiomedLife_2021
650		7	_2embne _9158738 _aProceso en lenguaje natural (Informática)
650		7	_2embne _aInteligencia artificial _9413115
700	1		_aCambria, Erik _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _993992
776	0	8	_iPrinted edition: _z9783030789602
776	0	8	_iPrinted edition: _z9783030789626
776	0	8	_iPrinted edition: _z9783030789633
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-78961-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2022 _dz _ek _zSI