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In February 2021, Google Search added a new interface feature to support the evaluation of web domains, known as the “About this result” feature. A prominent part of this feature is a snippet of text pulled automatically from Wikipedia, if a Wiki page for the web domain exists. While conducting large-scale audits of Google Search, we discovered that less than 40% of web domains shown in Google Search results contain a Wikipedia page. Then, we retrieved their Wikidata entries and looked at the extent they incorporate features related to W3C credibility signals. The lack of information for many signals points out to avenues for expanding Wikidata coverage.
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Incorporating Multimodal Information in Open-Domain Web Keyphrase Extraction
Open-domain Keyphrase extraction (KPE) on the Web is a fundamental yet complex NLP task with a wide range of practical applications within the field of Information Retrieval. In contrast to other document types, web page designs are intended for easy navigation and information finding. Effective designs encode within the layout and formatting signals that point to where the important information can be found. In this work, we propose a modeling approach that leverages these multi-modal signals to aid in the KPE task. In particular, we leverage both lexical and visual features (e.g., size, font, position) at the micro-level to enable effective strategy induction, and metalevel features that describe pages at a macrolevel to aid in strategy selection. Our evaluation demonstrates that a combination of effective strategy induction and strategy selection within this approach for the KPE task outperforms state-of-the-art models. A qualitative post-hoc analysis illustrates how these features function within the model.
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- PAR ID:
- 10295169
- Date Published:
- Journal Name:
- Incorporating Multimodal Information in Open-Domain Web Keyphrase Extraction
- Page Range / eLocation ID:
- 1790 to 1800
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2020.emnlp-main.140
