Semantic Query Labeling is the task of locating the constituent parts of a query and assigning domain-specific semantic labels to each of them. It allows unfolding the relations between the query terms and the documents’ structure while leaving unaltered the keyword-based query formulation. In this paper, we investigate the pre-training of a semantic query-tagger with synthetic data generated by leveraging the documents’ structure. By simulating a dynamic environment, we also evaluate the consistency of performance improvements brought by pre-training as real-world training data becomes available. The results of our experiments suggest both the utility of pre-training with synthetic data and its improvements’ consistency over time.

Bassani, E., Pasi, G. (2022). Evaluating the Use of Synthetic Queries for Pre-training a Semantic Query Tagger. In Advances in Information Retrieval. ECIR 2022 (pp.39-46). GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND : Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-99739-7_5].

Evaluating the Use of Synthetic Queries for Pre-training a Semantic Query Tagger

Bassani E.
Primo
;
Pasi G.
Ultimo
2022

Abstract

Semantic Query Labeling is the task of locating the constituent parts of a query and assigning domain-specific semantic labels to each of them. It allows unfolding the relations between the query terms and the documents’ structure while leaving unaltered the keyword-based query formulation. In this paper, we investigate the pre-training of a semantic query-tagger with synthetic data generated by leveraging the documents’ structure. By simulating a dynamic environment, we also evaluate the consistency of performance improvements brought by pre-training as real-world training data becomes available. The results of our experiments suggest both the utility of pre-training with synthetic data and its improvements’ consistency over time.
No
paper
Query generation; Semantic query labeling; Vertical search;
English
44th European Conference on Information Retrieval, ECIR 2022 - 10 April 2022 through 14 April 2022
978-3-030-99738-0
Bassani, E., Pasi, G. (2022). Evaluating the Use of Synthetic Queries for Pre-training a Semantic Query Tagger. In Advances in Information Retrieval. ECIR 2022 (pp.39-46). GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND : Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-99739-7_5].
Bassani, E; Pasi, G
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/392846
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