Dynamic Facet Selection by Maximizing Graded Relevance

Michael Glass, Md Faisal Mahbub Chowdhury, Yu Deng, Ruchi Mahindru, Nicolas Rodolfo Fauceglia, Alfio Gliozzo, Nandana Mihindukulasooriya


Abstract
Dynamic faceted search (DFS), an interactive query refinement technique, is a form of Human–computer information retrieval (HCIR) approach. It allows users to narrow down search results through facets, where the facets-documents mapping is determined at runtime based on the context of user query instead of pre-indexing the facets statically. In this paper, we propose a new unsupervised approach for dynamic facet generation, namely optimistic facets, which attempts to generate the best possible subset of facets, hence maximizing expected Discounted Cumulative Gain (DCG), a measure of ranking quality that uses a graded relevance scale. We also release code to generate a new evaluation dataset. Through empirical results on two datasets, we show that the proposed DFS approach considerably improves the document ranking in the search results.
Anthology ID:
2021.internlp-1.5
Volume:
Proceedings of the First Workshop on Interactive Learning for Natural Language Processing
Month:
August
Year:
2021
Address:
Online
Venue:
InterNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
32–39
Language:
URL:
https://aclanthology.org/2021.internlp-1.5
DOI:
10.18653/v1/2021.internlp-1.5
Bibkey:
Cite (ACL):
Michael Glass, Md Faisal Mahbub Chowdhury, Yu Deng, Ruchi Mahindru, Nicolas Rodolfo Fauceglia, Alfio Gliozzo, and Nandana Mihindukulasooriya. 2021. Dynamic Facet Selection by Maximizing Graded Relevance. In Proceedings of the First Workshop on Interactive Learning for Natural Language Processing, pages 32–39, Online. Association for Computational Linguistics.
Cite (Informal):
Dynamic Facet Selection by Maximizing Graded Relevance (Glass et al., InterNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.internlp-1.5.pdf
Code
 ibm/stackoverflow-technotes-dataset