Bew: Towards Answering Business-Entity-Related Web Questions
Qingqing Cao, Oriana Riva, Aruna Balasubramanian, Niranjan, Balasubramanian

TL;DR
BewQA is a system designed to answer business-related web questions by automatically extracting webpage templates and guiding search, significantly outperforming existing QA models and search engines in accuracy.
Contribution
The paper introduces a novel template-based approach for answering dynamic, business-related questions without requiring training, leveraging aggregator websites for template extraction.
Findings
27% improvement in F1 score over state-of-the-art QA models
29% more correct answers than a commercial search engine
Crowdsourced dataset of 1066 Bew questions in the restaurant domain
Abstract
We present BewQA, a system specifically designed to answer a class of questions that we call Bew questions. Bew questions are related to businesses/services such as restaurants, hotels, and movie theaters; for example, "Until what time is happy hour?". These questions are challenging to answer because the answers are found in open-domain Web, are present in short sentences without surrounding context, and are dynamic since the webpage information can be updated frequently. Under these conditions, existing QA systems perform poorly. We present a practical approach, called BewQA, that can answer Bew queries by mining a template of the business-related webpages and using the template to guide the search. We show how we can extract the template automatically by leveraging aggregator websites that aggregate information about business entities in a domain (e.g., restaurants). We answer a…
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Taxonomy
TopicsTopic Modeling · Web Data Mining and Analysis · Natural Language Processing Techniques
