Effective Semantics for Engineering NLP Systems
André Freitas
University of Manchester
At the center of many Natural Language Processing (NLP) applications is the requirement for capturing and interpreting commonsense and domain-specific knowledge at scale. The selection of the right semantic and knowledge representation model plays a strategic role for building NLP systems ( e.g. Question Answering, Sentiment Analysis, Semantic Search) which effectively work with real data.
In this talk, we will provide an overview of emerging trends in semantic representation for building NLP systems which can cope with large-scale and heterogeneous textual data. Based on empirical evidence, we will provide a description of the strengths and weaknesses of different representation perspectives, aiming towards a synthesis: 'a semantic model to rule them all'.
Week 26 2017/2018
Thursday 31st May 2018
3:00-4:00pm
Infolab D55
Joint Data Science Group and UCREL talk.