semantic role labelling - SRL

www.cs.brandeis.edu/~cs114/slides/SemanticRoleLabeling.ppt

Semantic Role Labeling – Giving Semantic Labels to Phrases
[AGENT Sotheby’s] offered [THEME a money-back guarantee] to [RECIPIENT the Dorrance heirs]

Applications
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Question Answering
Q: When was Napoleon defeated?
Look for: [PATIENT Napoleon] [PRED defeat-synset] [ARGM-TMP *ANS*]

Machine Translation
English (SVO) Farsi (SOV)
[AGENT The little boy] [AGENT pesar koocholo] boy-little
[PRED kicked] [THEME toop germezi] ball-red
[THEME the red ball] [ARGM-MNR moqtam] hard-adverb
[ARGM-MNR hard] [PRED zaad-e] hit-past

Document Summarization
Predicates and Heads of Roles summarize content

Information Extraction
SRL can be used to construct useful rules for IE

Annotations Used
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Syntactic Parsers
Collins’, Charniak’s (most systems) CCG parses ([Gildea & Hockenmaier 03],[Pradhan et al. 05]) TAG parses ([Chen & Rambow 03])

Shallow parsers
[NPYesterday] , [NPKristina] [VPhit] [NPScott] [PPwith] [NPa baseball].

Semantic ontologies (WordNet, automatically derived), and named entity classes
(v) hit (cause to move by striking)

propel, impel (cause to move forward with force)


Subtasks
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Identification:
Very hard task: to separate the argument substrings from the rest in this exponentially sized set
Usually only 1 to 9 (avg. 2.7) substrings have labels ARG and the rest have NONE for a predicate
Classification:
Given the set of substrings that have an ARG label, decide the exact semantic label

http://research.microsoft.com/pubs/101987/SRL-Tutorial-AAAI-07-0723.pdf