Type Prediction Systems
This work addresses the need for scalable type prediction in NLP, offering a solution that can handle large and varied type systems, though it appears incremental as it builds on prior supervised approaches.
The paper tackles the problem of predicting semantic types for entity mentions in text, which is important for downstream NLP tasks, by introducing two systems: an unsupervised TypeSuggest module for query terms and an Answer Type prediction module for queries, both designed to generalize to arbitrary type systems of any size.
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.