| 000 | 03863nam a22005415i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-02135-0 | ||
| 003 | DE-He213 | ||
| 005 | 20240730163820.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 220601s2010 sz | s |||| 0|eng d | ||
| 020 |
_a9783031021350 _9978-3-031-02135-0 |
||
| 024 | 7 |
_a10.1007/978-3-031-02135-0 _2doi |
|
| 050 | 4 | _aQ334-342 | |
| 050 | 4 | _aTA347.A78 | |
| 072 | 7 |
_aUYQ _2bicssc |
|
| 072 | 7 |
_aCOM004000 _2bisacsh |
|
| 072 | 7 |
_aUYQ _2thema |
|
| 082 | 0 | 4 |
_a006.3 _223 |
| 100 | 1 |
_aPalmer, Martha. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _980634 |
|
| 245 | 1 | 0 |
_aSemantic Role Labeling _h[electronic resource] / _cby Martha Palmer, Daniel Gildea, Nianwen Xue. |
| 250 | _a1st ed. 2010. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2010. |
|
| 300 |
_aIX, 95 p. _bonline resource. |
||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 347 |
_atext file _bPDF _2rda |
||
| 490 | 1 |
_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. | ||
| 650 | 0 |
_aArtificial intelligence. _93407 |
|
| 650 | 0 |
_aNatural language processing (Computer science). _94741 |
|
| 650 | 0 |
_aComputational linguistics. _96146 |
|
| 650 | 1 | 4 |
_aArtificial Intelligence. _93407 |
| 650 | 2 | 4 |
_aNatural Language Processing (NLP). _931587 |
| 650 | 2 | 4 |
_aComputational Linguistics. _96146 |
| 700 | 1 |
_aGildea, Daniel. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _980635 |
|
| 700 | 1 |
_aXue, Nianwen. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _980636 |
|
| 710 | 2 |
_aSpringerLink (Online service) _980637 |
|
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031010071 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031032639 |
| 830 | 0 |
_aSynthesis Lectures on Human Language Technologies, _x1947-4059 _980638 |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-02135-0 |
| 912 | _aZDB-2-SXSC | ||
| 942 | _cEBK | ||
| 999 |
_c85000 _d85000 |
||