Kuansan Wang


2020

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Explainable and Sparse Representations of Academic Articles for Knowledge Exploration
Keng-Te Liao | Zhihong Shen | Chiyuan Huang | Chieh-Han Wu | PoChun Chen | Kuansan Wang | Shou-de Lin
Proceedings of the 28th International Conference on Computational Linguistics

We focus on a recently deployed system built for summarizing academic articles by concept tagging. The system has shown great coverage and high accuracy of concept identification which could be contributed by the knowledge acquired from millions of publications. Provided with the interpretable concepts and knowledge encoded in a pre-trained neural model, we investigate whether the tagged concepts can be applied to a broader class of applications. We propose transforming the tagged concepts into sparse vectors as representations of academic documents. The effectiveness of the representations is analyzed theoretically by a proposed framework. We also empirically show that the representations can have advantages on academic topic discovery and paper recommendation. On these applications, we reveal that the knowledge encoded in the tagging system can be effectively utilized and can help infer additional features from data with limited information.

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CORD-19: The COVID-19 Open Research Dataset
Lucy Lu Wang | Kyle Lo | Yoganand Chandrasekhar | Russell Reas | Jiangjiang Yang | Doug Burdick | Darrin Eide | Kathryn Funk | Yannis Katsis | Rodney Michael Kinney | Yunyao Li | Ziyang Liu | William Merrill | Paul Mooney | Dewey A. Murdick | Devvret Rishi | Jerry Sheehan | Zhihong Shen | Brandon Stilson | Alex D. Wade | Kuansan Wang | Nancy Xin Ru Wang | Christopher Wilhelm | Boya Xie | Douglas M. Raymond | Daniel S. Weld | Oren Etzioni | Sebastian Kohlmeier
Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020

The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 200K times and has served as the basis of many COVID-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and describe several shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for COVID-19.

2018

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A Web-scale system for scientific knowledge exploration
Zhihong Shen | Hao Ma | Kuansan Wang
Proceedings of ACL 2018, System Demonstrations

To enable efficient exploration of Web-scale scientific knowledge, it is necessary to organize scientific publications into a hierarchical concept structure. In this work, we present a large-scale system to (1) identify hundreds of thousands of scientific concepts, (2) tag these identified concepts to hundreds of millions of scientific publications by leveraging both text and graph structure, and (3) build a six-level concept hierarchy with a subsumption-based model. The system builds the most comprehensive cross-domain scientific concept ontology published to date, with more than 200 thousand concepts and over one million relationships.

2010

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An Overview of Microsoft Web N-gram Corpus and Applications
Kuansan Wang | Chris Thrasher | Evelyne Viegas | Xiaolong Li | Bo-june Paul Hsu
Proceedings of the NAACL HLT 2010 Demonstration Session

2004

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Use and Acquisition of Semantic Language Model
Kuansan Wang | Ye-Yi Wang | Alex Acero
Demonstration Papers at HLT-NAACL 2004

2002

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SALT: An XML Application for Web-based Multimodal Dialog Management
Kuansan Wang
COLING-02: The 2nd Workshop on NLP and XML (NLPXML-2002)