In this manuscript, we review the work we undertake to build a large-scale benchmark dataset for an understudied Information Retrieval task called Semantic Query Labeling. This task is particularly relevant for search tasks that involve structured documents, such as Vertical Search, and consists of automatically recognizing the parts that compose a query and unfolding the relations between the query terms and the documents' fields. We first motivate the importance of building novel evaluation datasets for less popular Information Retrieval tasks. Then, we give an in-depth description of the procedure we followed to build our dataset.
Bassani, E., Pasi, G. (2021). On building benchmark datasets for understudied information retrieval tasks: The case of semantic query labeling. In Proceedings of the 11th Italian Information Retrieval Workshop 2021. CEUR-WS.
On building benchmark datasets for understudied information retrieval tasks: The case of semantic query labeling
Pasi G.
2021
Abstract
In this manuscript, we review the work we undertake to build a large-scale benchmark dataset for an understudied Information Retrieval task called Semantic Query Labeling. This task is particularly relevant for search tasks that involve structured documents, such as Vertical Search, and consists of automatically recognizing the parts that compose a query and unfolding the relations between the query terms and the documents' fields. We first motivate the importance of building novel evaluation datasets for less popular Information Retrieval tasks. Then, we give an in-depth description of the procedure we followed to build our dataset.
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