Semantic Role Labeling
Martha Palmer · Daniel Gildea · Nianwen Xue
mai 2022 · Springer Nature
E-book
95
Pages
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À propos de cet e-book
This 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
À propos de l'auteur
Martha Palmer is a Professor of Linguistics and Computer Science, and a Fellow of the Institute of Cognitive Science at the University of Colorado. Her current research is aimed at building domain-independent and language independent techniques for semantic interpretation based on linguistically annotated data used for training supervised systems, such as Proposition Banks. She has been the PI on projects to build Chinese, Korean and Hindi TreeBanks and English, Chinese, Korean, Arabic and Hindi Proposition Banks. She has been a member of the Advisory Committee for the DARPA TIDES program, Chair of SIGLEX, Chair of SIGHAN, and is a past President of the Association for Computational Linguistics. She was formerly an Associate Professor in Computer and Information Sciences at the University of Pennsylvania and received her Ph.D. in Artificial Intelligence from the University of Edinburgh in 1985.
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