Scientific Relation Extraction with Selectively Incorporated Concept Embeddings
This work addresses relation extraction in scientific papers, an incremental improvement for the NLP community.
The paper tackled scientific relation extraction by extending an existing model with character-level encoding attention on concept embeddings, achieving second place in classification and first in extraction in the SemEval 2018 shared task.
This paper describes our submission for the SemEval 2018 Task 7 shared task on semantic relation extraction and classification in scientific papers. We extend the end-to-end relation extraction model of (Miwa and Bansal) with enhancements such as a character-level encoding attention mechanism on selecting pretrained concept candidate embeddings. Our official submission ranked the second in relation classification task (Subtask 1.1 and Subtask 2 Senerio 2), and the first in the relation extraction task (Subtask 2 Scenario 1).