TextData: Save What You Know and Find What You Don't

被引：0

作者：

Ros, Kevin ^{[1
]}

Takwane, Kedar ^{[1
]}

Patil, Ashwin ^{[1
]}

Jayaprakash, Rakshana ^{[1
]}

Zhai, ChengXiang ^{[1
]}

机构：

[1] Univ Illinois, Urbana, IL 61801 USA

来源：

PROCEEDINGS OF THE 47TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, SIGIR 2024 | 2024年

关键词：

information retrieval; note-taking; interactive search; in-context search; question answering; recommendation; social bookmarking; SUPPORT;

D O I：

10.1145/3626772.3657681

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：2806 / 2810

页数：5

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