TextData: Save What You Know and Find What You Don't
Authors: 
Kevin Ros, Kedar Takwane, Ashwin Patil, Rakshana Jayaprakash, ChengXiang ZhaiAuthors Info & Claims
Kevin Ros, Kedar Takwane, Ashwin Patil, Rakshana Jayaprakash, ChengXiang ZhaiAuthors Info & ClaimsPages 2806 - 2810
Abstract
In this demonstration, we present TextData, a novel online system that enables users to both "save what they know" and "find what they don't". TextData was developed based on the Community Digital Library (CDL) system. Although the CDL allowed users to bookmark webpages with plain text and provided search and recommendation, it fell short in key features. To better help users save what they know, TextData offers the addition of markdown to submissions for providing a richer method of note-taking. To better help users find what they don't, TextData provides methods for visualizing the relationships among submissions and provides in-context interactive search intent prediction with question-answering via a generative large language model. TextData is free-to-use, can be accessed online, and the source code is publicly available.
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- TextData: Save What You Know and Find What You Don't
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Information & Contributors
Information
Published In
July 2024
3164 pages
ISBN:9798400704314
DOI:10.1145/3626772
- General Chairs:
- Grace Hui Yang,
- Hongning Wang,
- Sam Han,
- Program Chairs:
- Claudia Hauff,
- Guido Zuccon,
- Yi Zhang
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.
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Association for Computing Machinery
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Publication History
Published: 11 July 2024
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- National Science Foundation
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SIGIR 2024
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SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 14 - 18, 2024
Washington DC, USA
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