# Target Type Identification for Entity-Bearing Queries

arXiv: https://arxiv.org/abs/1705.06056 · HTML: https://mlsanity.com/paper/1705.06056/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2017.

## Built on by (most cited first, 2 of 2)

- [Semantic Answer Type Prediction using BERT: IAI at the ISWC SMART Task 2020](https://mlsanity.com/paper/2109.06714/) (2021)
- [Extreme Classification for Answer Type Prediction in Question Answering](https://mlsanity.com/paper/2304.12395/) (2023)

## Abstract (arXiv)

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.