@inproceedings{glass-etal-2021-dynamic,
title = "Dynamic Facet Selection by Maximizing Graded Relevance",
author = "Glass, Michael and
Chowdhury, Md Faisal Mahbub and
Deng, Yu and
Mahindru, Ruchi and
Fauceglia, Nicolas Rodolfo and
Gliozzo, Alfio and
Mihindukulasooriya, Nandana",
editor = {Brantley, Kiant{\'e} and
Dan, Soham and
Gurevych, Iryna and
Lee, Ji-Ung and
Radlinski, Filip and
Sch{\"u}tze, Hinrich and
Simpson, Edwin and
Yu, Lili},
booktitle = "Proceedings of the First Workshop on Interactive Learning for Natural Language Processing",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.internlp-1.5/",
doi = "10.18653/v1/2021.internlp-1.5",
pages = "32--39",
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."
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%0 Conference Proceedings
%T Dynamic Facet Selection by Maximizing Graded Relevance
%A Glass, Michael
%A Chowdhury, Md Faisal Mahbub
%A Deng, Yu
%A Mahindru, Ruchi
%A Fauceglia, Nicolas Rodolfo
%A Gliozzo, Alfio
%A Mihindukulasooriya, Nandana
%Y Brantley, Kianté
%Y Dan, Soham
%Y Gurevych, Iryna
%Y Lee, Ji-Ung
%Y Radlinski, Filip
%Y Schütze, Hinrich
%Y Simpson, Edwin
%Y Yu, Lili
%S Proceedings of the First Workshop on Interactive Learning for Natural Language Processing
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F glass-etal-2021-dynamic
%X 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.
%R 10.18653/v1/2021.internlp-1.5
%U https://aclanthology.org/2021.internlp-1.5/
%U https://doi.org/10.18653/v1/2021.internlp-1.5
%P 32-39
Markdown (Informal)
[Dynamic Facet Selection by Maximizing Graded Relevance](https://aclanthology.org/2021.internlp-1.5/) (Glass et al., InterNLP 2021)
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.