Abstract.

The paper describes the research of methods for semantic role labeling based on semi-supervised machine learning. We present a method for training semantic role labeler using corpus automatically annotated by baseline dictionary-based (rule-based) semantic parser that improves the performance of the baseline. We also propose a method for labeling arguments of “unknown” predicates that are not present in the semantic dictionary of the baseline parser. The hybrid semantic parser is presented. It uses two models for “known” and “unknown” predicates as well as the dictionary-based parser. The experiments with the manually labeled test corpus in Russian show that modifications proposed in the paper improve recall and overall performance of semantic role labeling.

Keywords:

semantic role labeling, semi-supervised machine learning, semantic parsing, word embedding.

PP. 104-120.

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