000			03560nam a22004335i 4500
999			_c387361 _d387361
001			387361
003			ES-MaUEC
005			20230315164515.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			220601s2010 sz | s |||| 0|eng d
020			_a9783031021350
024	7		_a10.1007/978-3-031-02135-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aP325 _b2010 EB
100	1		_aPalmer, Martha Stone _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687380
245	1	0	_aSemantic Role Labeling _cby Martha Palmer, Daniel Gildea, Nianwen Xue
250			_a1st edition 2010
264		1	_aCham _bSpringer International Publishing _c2010
300			_a1 recurso en línea (IX, 95 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 -- Semantic Roles -- Available Lexical Resources -- Machine Learning for Semantic Role Labeling -- A Cross-Lingual Perspective -- Summary.
520			_aThis book is aimed at providing an overview of several aspects of semantic role labeling. Chapter 1 begins with linguistic background on the definition of semantic roles and the controversies surrounding them. Chapter 2 describes how the theories have led to structured lexicons such as FrameNet, VerbNet and the PropBank Frame Files that in turn provide the basis for large scale semantic annotation of corpora. This data has facilitated the development of automatic semantic role labeling systems based on supervised machine learning techniques. Chapter 3 presents the general principles of applying both supervised and unsupervised machine learning to this task, with a description of the standard stages and feature choices, as well as giving details of several specific systems. Recent advances include the use of joint inference to take advantage of context sensitivities, and attempts to improve performance by closer integration of the syntactic parsing task with semantic role labeling. Chapter 3 also discusses the impact the granularity of the semantic roles has on system performance. Having outlined the basic approach with respect to English, Chapter 4 goes on to discuss applying the same techniques to other languages, using Chinese as the primary example. Although substantial training data is available for Chinese, this is not the case for many other languages, and techniques for projecting English role labels onto parallel corpora are also presented. Table of Contents: Preface / Semantic Roles / Available Lexical Resources / Machine Learning for Semantic Role Labeling / A Cross-Lingual Perspective / Summary.
988			_aSynthesis Collection of Technology_2010
650		7	_2embne _9666075 _aLingüística computacional
650		7	_2embne _9140153 _aSemántica
700	1		_aGildea, Daniel _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687381
700	1		_aXue, Nianwen _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687382
776	0	8	_iPrinted edition: _z9783031010071
776	0	8	_iPrinted edition: _z9783031032639
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02135-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI