Name
Abstract | ||
|---|---|---|
Uncertainty in a text refers to the propositions that exhibit fuzziness in meaning. In this paper, we present a novel Multi-Channel Tree-LSTM model that integrates a relation aware self-attention along with multiple embeddings to automatically detect uncertainty cues in texts. We have evaluated the models with data sources across multiple domains that include bio-medical texts, privacy policies, and consumer reviews. Our preliminary analysis showed that the proposed model outperforms the existing baseline systems across all the domains.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3366424.3382713 | WWW '20: The Web Conference 2020
Taipei
Taiwan
April, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7024-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manjira Sinha | 1 | 22 | 12.94 |
| Tirthankar Dasgupta | 2 | 76 | 26.41 |