The experimental results show that our model achieves an F-score of 59.61% on the commonly used dataset (MLEE) of biomedical event extraction, which outperforms ...

Sep 9, 2019 · In this paper, we propose a BLSTM neural network inte- grating multi-level attention mechanism and dependency- based word embeddings to extract ...

Therefore, we employ a Bidirectional Long Short Term Memory (BLSTM) neural network for event extraction, which can skip handcrafted complex feature extraction.

Sep 1, 2019 · Therefore, we employ a Bidirectional Long Short Term Memory (BLSTM) neural network for event extraction, which can skip handcrafted complex ...

To skip the manual complex feature extraction, we propose a trigger identification method based on Bidirectional Long Short Term Memory (BLSTM) neural network.

Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger ...

<p>Biomedical event extraction is an important and challenging task in Information Extraction, which plays a key role for medicine research and disease ...

This study aims to develop a unified framework based on event constraint information that jointly extract biomedical event triggers and arguments.

We propose an end-to-end neural nested event extraction model named DeepEventMine that extracts multiple overlapping directed acyclic graph structures from a ...

Apr 5, 2022 · The experimental results on the multi-level event extraction (MLEE) corpus show that the proposed method outperforms the state-of-the-art ...