- Mathematical Foundations: Formal Grammars and Languages
- Finite-State Technology
- Statistical Methods: Fundamentals
- Statistical Models for Natural Language Processing
- Machine Learning
- Word Representation
- Deep Learning
- Sublanguages and Controlled Languages
- Corpus Annotation
- Text Segmentation
- Part-of-Speech Tagging
- Semantic Role Labelling
- Word Sense Disambiguation
- Computational Treatment of Multiword Expressions
- Textual Entailment
- Natural Language Generation
- Speech Recognition
- Temporal Processing
- Text-to-Speech Synthesis
- Machine Translation
- Translation Technology
- Information Retrieval
- Information Extraction
- Question Answering
- Text Summarization
- Term Extraction
- Web Text Mining
- Opinion Mining and Sentiment Analysis
- Spoken Language Dialogue Systems
- Multimodal Systems
- Natural Language Processing for Educational Applications
- Automated Writing Assistance
- Text Simplification
- Author Profiling and Related Applications
Abstract and Keywords
This chapter provides a summary of the available annotated resources for training supervised approaches to automatic semantic role labelling, and their theoretical underpinnings. It compares three main types of semantic role labelled resources: the PropBank, VerbNet, and FrameNet. It also surveys the different techniques that have been used to build supervised systems, as well as less supervised approaches. It examines the syntactic representations, features, and classifiers that are typically used in semantic role labelling systems. It concludes with a discussion of other languages, and issues that need to be considered when applying semantic role labelling cross-linguistically. It also discusses issues with the alignment of semantic roles in two languages and the projection of semantic roles from one language to another.
Martha Palmer, Professor of Linguistics and Computer Science, University of Colorado at Boulder
Sameer Pradhan holds a joint appointment at the Harvard Medical School as an Instructor, and at the affiliated Boston Children's Hospital as a Associate Scientific Researcher.
Xue, Nianwen (薛念文) is based in the Computer Science Department and the Language and Linguistics Program, Brandeis University. His research interests include syntactic, semantic, temporal and discourse annotation, semantic-role labeling and machine translation. He has published work on Chinese word segmentation and semantic parsing using statistical machine-learning techniques.
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