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Meet up

Building a Knowledge Base Question Answering Pipeline

Monday, 28th January at CodeNode, London

This meetup was organised by London Data Science Journal Club in January 2019

Building a Knowledge Base Question Answering Pipeline

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: * V6ooutqdmhpbjtwq7xib Nlb7mk11dhiefgbrbopg

Attending Members

Overview

Building a Knowledge Base Question Answering Pipeline

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: * V6ooutqdmhpbjtwq7xib Nlb7mk11dhiefgbrbopg

Who's coming?

Attending Members