SkillsCast

Building a Knowledge Base Question Answering Pipeline

28th January 2019 in London at CodeNode

This SkillsCast was filmed at Building a Knowledge Base Question Answering Pipeline

This session was not filmed.

In this session, we will discuss a semantic parsing approach to knowledge base question answering and the challenges of building a question answering pipeline.

Knowledge base question answering aims to provide a natural language interface to factual knowledge. It requires precise modeling of the question meaning through the entities and relations available in the knowledge base in order to retrieve the correct answer.

In this session, we will discuss a semantic parsing approach to knowledge base question answering and the challenges of building a question answering pipeline. It is common to break down the task into three main steps: entity linking, relation disambiguation and answer retrieval. We will focus on two aspects: entity linking across various categories of entities and using graph neural networks to jointly encode entities and relations.

Papers:

Code: * https://github.com/UKPLab/starsem2018-entity-linking * https://github.com/UKPLab/coling2018-graph-neural-networks-question-answering

Further Reading: * Scala eXchange London 2019 (in London on 12th - 13th December 2019)

  • Connected Data London May (in London on 21st May 2019)
  • Fast Track to Machine Learning with Louis Dorard (in London on 2nd - 4th December 2019)
  • Thanks to our sponsors

    SkillsCast

    This session was not filmed.

    In this session, we will discuss a semantic parsing approach to knowledge base question answering and the challenges of building a question answering pipeline.

    Knowledge base question answering aims to provide a natural language interface to factual knowledge. It requires precise modeling of the question meaning through the entities and relations available in the knowledge base in order to retrieve the correct answer.

    In this session, we will discuss a semantic parsing approach to knowledge base question answering and the challenges of building a question answering pipeline. It is common to break down the task into three main steps: entity linking, relation disambiguation and answer retrieval. We will focus on two aspects: entity linking across various categories of entities and using graph neural networks to jointly encode entities and relations.

    Papers:

    Code: * https://github.com/UKPLab/starsem2018-entity-linking * https://github.com/UKPLab/coling2018-graph-neural-networks-question-answering

    Further Reading: * Scala eXchange London 2019 (in London on 12th - 13th December 2019)

  • Connected Data London May (in London on 21st May 2019)
  • Fast Track to Machine Learning with Louis Dorard (in London on 2nd - 4th December 2019)
  • Thanks to our sponsors