Name
Abstract | ||
|---|---|---|
We present a browser-based scientific article search system with graphical visualization. This system is based on triples of distributed representations of articles, each triple representing a scientific discourse facet (Objective, Method, or Result) using both text and citation information. Because each facet of an article is encoded as a separate vector, the similarity between articles can be measured by considering the articles not only in their entirety but also on a facet-by-facet basis. Our system provides three search options: a similarity ranking search, a citation graph with facet-labeled edges, and a scatter plot visualization with facets as the axes. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1609/aaai.v33i01.33019859 | AAAI |
Field | DocType | Volume |
Monad (category theory),Information retrieval,Ranking,Computer science,Visualization,Citation,Artificial intelligence,Facet (geometry),Citation graph,Scatter plot,Machine learning | Conference | 33 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuta Kobayashi | 1 | 2 | 0.70 |
| Hiroyuki Shindo | 2 | 75 | 13.80 |
| Yuji Matsumoto | 3 | 17 | 2.93 |