Semantic Role Labeling

Semantic Role Labeling is a multidisciplinary topic between Artifical Intelligence (Natural Language Processing and Understanding) and Linguistics Semantics. Semantic Role Labelling deals with the semantic analysis of sentences, often, for practical applications, such as Information Extraction, Question Answering, Information Retrieval, Text Summarization and so on. In this analysis, words and phrases in a sentence are labeled with Conceptual Roles such as Agent, Theme, Patient, Cause, etc. hoping that they can provide for a minimum level of language understanding (particularly for machines) so that questions such as who, whom, why, what, when, where, how, etc. can be answered in a systematic way.

This is an introductory course in which we will address three major topics:

  • - Defining key concepts in semantic role labeling and argument-structure analysis
  • - Introducing lexical knowledge bases that are developed for this purpose
  • - Introducing computational methods, mostly supervised machine learning techniques, for developing semantic role labeler



The content will be offered in a way that it can be helpful to the audience of different background (Linguistics, Information Science, Computational Linguistics).



Download slides for Session 1 here
Download slides for Session 2 here
Download slides for Session 3 here
Download slides for Session 4 here



This page last edited on 10 November 2018.

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