Name
Abstract | ||
|---|---|---|
Short texts, such as Twitter social media posts, have become increasingly popular on the Internet. Inferring topics from massive numbers of short texts is important to many real-world applications. A single short text often contains a few words, making traditional topic models less effective. A recently developed biterm topic model (BTM) effectively models short texts by capturing the rich global ... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1093/comjnl/bxy037 | The Computer Journal |
Keywords | Field | DocType |
short text,topic modeling,word embeddings,clustering,text similarity | Information retrieval,Computer science,Theoretical computer science,Biterm topic model,Topic model | Journal |
Volume | Issue | ISSN |
62 | 3 | 0010-4620 |
Citations | PageRank | References |
3 | 0.39 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ximing Li | 1 | 11 | 5.37 |
| Ang Zhang | 2 | 9 | 1.23 |
| Changchun Li | 3 | 10 | 2.66 |
| Lantian Guo | 4 | 13 | 1.66 |
| Wenting Wang | 5 | 233 | 25.66 |
| Jihong OuYang | 6 | 94 | 15.66 |