Type Prediction Systems

TL;DR

This paper introduces two versatile systems for predicting semantic types in text, capable of handling large and arbitrary type systems, enhancing various NLP tasks like entity disambiguation and question answering.

Contribution

The work presents two novel, scalable, and unsupervised type prediction systems that generalize to any size of type systems, unlike prior supervised approaches.

Findings

Systems successfully predict types for user queries.

They generalize to large and arbitrary type systems.

Applicable to multiple NLP downstream tasks.

Abstract

Inferring semantic types for entity mentions within text documents is an important asset for many downstream NLP tasks, such as Semantic Role Labelling, Entity Disambiguation, Knowledge Base Question Answering, etc. Prior works have mostly focused on supervised solutions that generally operate on relatively small-to-medium-sized type systems. In this work, we describe two systems aimed at predicting type information for the following two tasks, namely, a TypeSuggest module, an unsupervised system designed to predict types for a set of user-entered query terms, and an Answer Type prediction module, that provides a solution for the task of determining the correct type of the answer expected to a given query. Our systems generalize to arbitrary type systems of any sizes, thereby making it a highly appealing solution to extract type information at any granularity.