# Target Type Identification for Entity-Bearing Queries

## TL;DR

This paper presents a supervised learning method for automatically identifying target types in entity-bearing queries, significantly improving retrieval performance by leveraging a rich feature set and a purpose-built test collection.

## Contribution

It introduces a novel supervised approach with diverse features for target type detection in queries, outperforming previous methods.

## Key findings

- Outperforms existing methods by a significant margin
- Uses a purpose-built test collection for evaluation
- Demonstrates effectiveness of rich feature sets in query type detection

## Abstract

Identifying the target types of entity-bearing queries can help improve retrieval performance as well as the overall search experience. In this work, we address the problem of automatically detecting the target types of a query with respect to a type taxonomy. We propose a supervised learning approach with a rich variety of features. Using a purpose-built test collection, we show that our approach outperforms existing methods by a remarkable margin. This is an extended version of the article published with the same title in the Proceedings of SIGIR'17.