In this paper, we report on the solution [ST] we submitted to the WNUT 2016 NER shared task.
Extraction of concepts present in patient clinical records is an essential step in clinical research.
The reported results in the shared task bring this submission to the third place on subtask 1 (word relatedness), and the first place on subtask 2 (semantic relation classification), demonstrating the utility of integrating the complementary path-based and distributional information sources in recognizing concrete semantic relations.
Recognizing various semantic relations between terms is beneficial for many NLP tasks.
Discriminating between closely-related language varieties is considered a challenging and important task.
The SemEval-2010 benchmark dataset has brought renewed attention to the task of automatic keyphrase extraction.
The shared task comprised of a total of 8 tracks, of which we participated in 7.
We propose an approach for named entity recognition in medical data, using a character-based deep bidirectional recurrent neural network.