Name
Abstract | ||
|---|---|---|
Effectively exploring and browsing document collections is a fundamental problem in visualization. Traditionally, document visualization is based on a data model that represents each document as the set of its comprised words, effectively characterizing what the document is. In this paper we take an alternative perspective: motivated by the manner in which users search documents in the research process, we aim to visualize documents via their usage, or how documents tend to be used. We present a new visualization scheme — cite2vec — that allows the user to dynamically explore and browse documents via how other documents use them, information that we capture through citation contexts in a document collection. Starting from a usage-oriented word-document 2D projection, the user can dynamically steer document projections by prescribing semantic concepts, both in the form of phrase/document compositions and document:phrase analogies, enabling the exploration and comparison of documents by their use. The user interactions are enabled by a joint representation of words and documents in a common high-dimensional embedding space where user-specified concepts correspond to linear operations of word and document vectors. Our case studies, centered around a large document corpus of computer vision research papers, highlight the potential for usage-based document visualization. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TVCG.2016.2598667 | IEEE Trans. Vis. Comput. Graph. |
Keywords | Field | DocType |
Two dimensional displays,Visualization,Context,Data visualization,Tracking,Object detection,Semantics | Data visualization,Well-formed document,Information retrieval,Visualization,Document management system,Document clustering,Computer science,Document layout analysis,Phrase,Semantics | Journal |
Volume | Issue | ISSN |
23 | 1 | 1077-2626 |
Citations | PageRank | References |
16 | 0.56 | 34 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew S. Berger | 1 | 20 | 3.67 |
| Katherine McDonough | 2 | 16 | 2.92 |
| Lee M. Seversky | 3 | 150 | 9.08 |