Modeling citation worthiness by using attention-based bidirectional long short-term memory networks and interpretable models
Bibliographic Data
| ID | 21442420 |
|---|---|
| Authors | Tong Zeng (0000-0002-5533-8506, Syracuse University), Daniel E Acuna (0000-0002-7765-1595, Syracuse University, corresponding author) |
| Year | 2020 |
| Volume | 124 |
| Issue | 1 |
| Pages | 399-428 |
| Publication date | 2020-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Scientometrics (JOURNAL) |
| Journal identifiers | ISSN: 0138-9130 • E-ISSN: 1588-2861 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11192-020-03421-9 |
| OpenAlex | W3014030660 |
| Language | EN |
| Citations received | 3 |
| References cited | 35 |
Artificial neural network · Citation · Cognition · Cognitive psychology · Cognitive science · Long short term memory · Short-term memory · Term (time) · Working memory · World Wide Web · Advanced Text Analysis Techniques · Artificial Intelligence · Biomedical Text Mining and Ontologies · Computer Science · Neuroscience · Psychology · Topic Modeling
| Citations per year | 0.75 |
|---|---|
| Citation span | 2022 - 2025 (4) |
| Highly cited | No |