Target Type Identification¶
A characteristic property of entities is that they are typed. Naturally, entity-bearing queries may be complemented with target types (types of its relevant entities). Given a search query, Target Type Identification (TTI) is the task of returning a ranked list of types from an underlying type taxonomy. Entity retrieval performance can be significantly improved when explicit target type information is identified for a query.
The following methods for target type identification are implemented in Nordlys:
- EC: the Entity Centric (EC) method, as described in [Balog and Neumayer, 2012]. Both BM25 and LM models can be used as a retrieval model. This method fits the late fusion design pattern in [Zhang and Balog, 2017].
- TC: the Type Centric (TC) method based on [Balog and Neumayer, 2012]. Both BM25 and LM models can be used as a retrieval model. This method fits the early fusion design pattern in [Zhang and Balog, 2017].
- LTR: the Learing-To-Rank (LTR) method, as proposed in [Garigliotti et al., 2017]. This method establishes the state-of-the-art performance in TTI.
Below, we present retrieval results on the target type identification in [Garigliotti et al., 2017].
|EC, BM25 (K = 20)||0.1490||0.3223|
|EC, LM (K = 20)||0.1417||0.3161|
The corresponding files with rankings can be found on Github, specifically under output directory.
- Darío Garigliotti, Faegheh Hasibi, and Krisztian Balog. Target Type Identification for Entity-Bearing Queries. In: 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’17). [BIB] [PDF]
- Krisztian Balog, Robert Neumayer. 2012. Hierarchical target type identification for entity-oriented queries. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM ‘12). 2391–2394. [BIB] [PDF]