# Type Prediction Systems

arXiv: https://arxiv.org/abs/2104.01207 · HTML: https://mlsanity.com/paper/2104.01207/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 2 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Ultra-Fine Entity Typing](https://mlsanity.com/paper/1807.04905/) (2018)
- [Web Table Extraction, Retrieval and Augmentation: A Survey](https://mlsanity.com/paper/2002.00207/) (2020)
- [SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge](https://mlsanity.com/paper/2012.00555/) (2020)

## Abstract (arXiv)

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.