Name
Abstract | ||
|---|---|---|
Medical professionals search the published literature by specifying the type of , the medical and the measure(s) of interest. In this paper we demonstrate how features encoding syntactic patterns improve the performance of state-of-the-art sequence tagging models (both linear and neural) for information extraction of these medically relevant categories. We present an analysis of the type of patterns exploited, and the semantic space induced for these, i.e., the distributed representations learned for identified multi-token patterns. We show that these learned representations differ substantially from those of the constituent unigrams, suggesting that the patterns capture contextual information that is otherwise lost. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.18653/v1/N18-2060 | north american chapter of the association for computational linguistics |
DocType | Volume | Issue |
Journal | abs/1805.00097 | Short Paper |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roma Patel | 1 | 1 | 1.71 |
| Yinfei Yang | 2 | 99 | 16.53 |
| Iain James Marshall | 3 | 42 | 8.06 |
| Ani Nenkova | 4 | 1831 | 109.14 |
| Byron C. Wallace | 5 | 401 | 27.72 |