Name
Abstract | ||
|---|---|---|
Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. However, many relation types, particularly in biomedical text, are expressed across sentences or require a large context to disambiguate. We propose a model to consider all mention and entity pairs simultaneously in order to make a prediction. We encode full paper abstracts using an efficient self-attention encoder and form pairwise predictions between all mentions with a bi-affine operation. An entity-pair wise pooling aggregates mention pair scores to make a final prediction while alleviating training noise by performing within document multi-instance learning. We improve our modelu0027s performance by jointly training the model to predict named entities and adding an additional corpus of weakly labeled data. We demonstrate our modelu0027s effectiveness by achieving the state of the art on the Biocreative V Chemical Disease Relation dataset for models without KB resources, outperforming ensembles of models which use hand-crafted features and additional linguistic resources. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computation and Language | ENCODE,Pairwise comparison,Information retrieval,Computer science,Pooling,Natural language processing,Encoder,Artificial intelligence,Labeled data,Sentence,Relationship extraction |
DocType | Volume | Citations |
Journal | abs/1710.08312 | 3 |
PageRank | References | Authors |
0.37 | 34 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patrick Verga | 1 | 97 | 9.11 |
| Emma Strubell | 2 | 82 | 7.10 |
| Ofer Shai | 3 | 48 | 9.74 |
| Andrew Kachites McCallumzy | 4 | 19203 | 1588.22 |