Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-030-99739-7_5 | ADVANCES IN INFORMATION RETRIEVAL, PT II |
Keywords | DocType | Volume |
Semantic query labeling, Query generation, Vertical search | Conference | 13186 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elias Bassani | 1 | 1 | 2.08 |
| Gabriella Pasi | 2 | 0 | 0.34 |