Publications

A Latent Model for Ad Hoc Table Retrieval

Ebrahim Bagheri and Feras Al-Obeidat
Reference:
Ebrahim Bagheri and Feras Al-Obeidat A Latent Model for Ad Hoc Table Retrieval. In 42nd European Conference on IR Research (ECIR 2020), 2020.
Links to Publication:
Abstract:
The ad hoc table retrieval task is concerned with satisfying a query with a ranked list of tables. While there are strong baselines in the literature that exploit learning to rank and semantic matching techniques, there are still a set of hard queries that are difficult for these baseline methods to address. We find that such hard queries are those whose constituting tokens (i.e., terms or entities) are not fully or partially observed in the relevant tables. We focus on proposing a latent factor model to address such hard queries. Our proposed model factorizes the token-table co-occurrence matrix into two low dimensional latent factor matrices that can be used for measuring table and query similarity even if no shared tokens exist between them. We find that the variation of our proposed model that considers keywords provides statistically significant improvement over three strong baselines in terms of NDCG and ERR.
Bibtex Entry:
@inproceedings{ecir2020b, author = {Ebrahim Bagheri and Feras Al-Obeidat}, title = {A Latent Model for Ad Hoc Table Retrieval}, booktitle = {42nd European Conference on IR Research (ECIR 2020)}, year = {2020}, abstract = {The ad hoc table retrieval task is concerned with satisfying a query with a ranked list of tables. While there are strong baselines in the literature that exploit learning to rank and semantic matching techniques, there are still a set of hard queries that are difficult for these baseline methods to address. We find that such hard queries are those whose constituting tokens (i.e., terms or entities) are not fully or partially observed in the relevant tables. We focus on proposing a latent factor model to address such hard queries. Our proposed model factorizes the token-table co-occurrence matrix into two low dimensional latent factor matrices that can be used for measuring table and query similarity even if no shared tokens exist between them. We find that the variation of our proposed model that considers keywords provides statistically significant improvement over three strong baselines in terms of NDCG and ERR.} }




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