Semantic Role Labeling: An Introduction to the Special Issue

®  Sentence + designated verb: SRL identify the arguments of the verb. And label them with semantic roles.

®  Steps:

¯  1 Pruning.

¯  2 Local scoring

¯  3 Joint scoring

(4) Fixing common errors

 

Pruning

®  Filtering the set of arguments for a given predicate.

¯  Any subsequence of words in the sentence is an argument candidate.

®  Xue and Palmer (2004)

Joint scoring      

®  Good structure of labeled arguments.

®  Arguments do not overlap,

®  Core arguments do not repeat, etc.

®  Re-ranking

®  Probabilistic methods.

Conditional random fields

SRL architecture:

®  Combination of systems and input annotations.

¯  Increase robustness,

¯  Gain coverage

¯  Reduce effects of parse errors

®  One my combine:

¯  Output of independents srl basic systems

¯  Outputs from same srl s. changing input annotations or parameters.

Gaing of 2-3 F1 points.  

®  Joint labeling…

®  Dependency parsing

®  Combine parsing and srl in a single step. …

®  Characterize candidate argument
phrase type, headword, …

®  Characterize verb predicate +cntx
lemma, voice,

®  Characterize the relation.
Syntactic + semantic.
Left/right position of the constituent  with respect to the verb…

®  Recall:

®  81% argument identification

®  95% assigned correct semantic role.

®  SemEval-2007

®  Disambiguation of 50 verbs.

®  FrameNet

®  40 frames

®  F1=92% asigning semantic roles

®  F1 83% segmenting and labeling arg.

®  Complete analysis of semantic roles on unseen texts,

®  Precision 60s

®  Recall 30s

®  SRL relies on syntactic structure.

®  Output by statistical parser 90% matching.

®  Is common to use parser trees.

®  Gold-standard trees.???

®  Most of the errors are by having incorrect syntactic constituents.

®  SRL relies on syntactic structure.

®  Output by statistical parser 90% matching.

®  Is common to use parser trees.

®  Gold-standard trees.???

®  Most of the errors are by having incorrect syntactic constituents.

®  CoNLL-2005 Brown corpus annotated

®  Performance drop below 70%

®  They clamed errors are in assigning the semantic roles rather than identification of argument boundaries.

®  Spanish and catalan, CESS-ECE corpus.

®  86% disambiguation predicates

®  83% labeling arguments.

®  chinese

®  semEval-2007 25K SENTENCE

®  80% core elements.

Top performing team use machine learning techniques