Automatic Semantic Role Labeling
Scott Wen-tau Yih and Kristina Toutanova
The goal of semantic role labeling is to map sentences to
domain-independent semantic representations, which abstract away from
syntactic structure and are important for deep NLP tasks such as
question answering, textual entailment, and complex information
extraction. Semantic role labeling has recently received significant
interest in the natural language processing community. In this
tutorial, we will first describe the problem and history of semantic
role labeling, and introduce existing corpora and other related tasks.
Next, we will provide a detailed survey of state-of-the-art machine
learning approaches to building a semantic role labeling system.
Finally, we will conclude the tutorial by discussing directions for
improving semantic role labeling systems and their application to
other natural language problems.
- What is semantic role labeling?
- Why is SRL important?
- Existing corpora: FrameNet & PropBank
- Corpora in development
- Relation to other tasks
- Survey of Existing SRL Systems
- History of the development of automatic SRL systems
- Pioneering Work
- Basic architecture of a generic SRL system
- Major components
- Machine learning technologies
- CoNLL-04 and CoNLL-05 shared tasks on SRL
- Details of several CoNLL-05 systems
- Overall comparisons of CoNLL-05 systems
- Analysis of Systems and Future Directions
- Error Analysis
- Influence of parser errors
- Per argument performance
- Directions for improving SRL
- Information Extraction
- Textual Entailment
- Machine Translation
The main target audience is NLP students and researchers who are
interested in learning about semantic role labeling, but have not
followed all developments in the field. Additionally, researchers
already working on semantic role labeling should profit from a global
view and summary of relevant work. The tutorial will also be valuable
for researchers working in the related areas of information extraction
and spoken language understanding.
Scott Wen-tau Yih received his PhD in Computer Science from the
University of Illinois at Urbana-Champaign in 2005 and is currently a
Post-Doc Researcher in the Machine Learning and Applied Statistics
group at Microsoft Research. His research focuses on different
problems in natural language processing and machine learning, such as
information extraction and semantic parsing. Scott has published
several papers on semantic role labeling in CoNLL-04&05, COLING-04 and
IJCAI-05. The SRL system he built at UIUC was the best system in the
CoNLL-05 shared task.
Kristina Toutanova obtained her PhD in Computer Science from Stanford
University in 2005 and joined Microsoft Research as a Researcher in
the Natural Language Processing group. Her areas of expertise include
semantic role labeling, syntactic parsing, machine learning, and
machine translation. Kristina has published two papers on semantic
role labeling in CoNLL-05 and ACL-05. The SRL system she built at
Stanford was the runner-up system in the CoNLL-05 shared task.