Semantic role labeling aims to model the predicate-argument structure of a sentence and is often described as answering "Who did what to whom". BIO notation is typically used for semantic role labeling.
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We present a semantic role labeling resource for Hebrew built semi-automatically through annotation projection from English.
Nominal roles are not labeled in the training data, and the learning objective instead pushes the labeler to assign roles predictive of the arguments.
Many efforts of research are devoted to semantic role labeling (SRL) which is crucial for natural language understanding.
A large number of natural language processing tasks exist to analyze syntax, semantics, and information content of human language.
We then train a prediction model using both utterances containing ellipsis and our automatically completed utterances.
Question-answer driven Semantic Role Labeling (QA-SRL) was proposed as an attractive open and natural flavour of SRL, potentially attainable from laymen.
In this paper, we define a new cross-style semantic role label convention and propose a new cross-style joint optimization model designed according to the linguistic meaning of semantic role, which provides an agreed way to make the results of two styles more comparable and let both types of SRL enjoy their natural connection on both linguistics and computation.