Context awareness and embedding for biomedical event extraction

Joint Extraction of Biomedical Events Based on Dynamic Path Planning Strategy and Hybrid Neural Network

Biomedical event detection is a pivotal information extraction task in molecular biology and biomedical research, which provides inspiration for the medical search, disease prevention, and new drug development. The existing methods usually detect simple biomedical events and complex events with the same model, and the performance of the complex biomedical event extraction is relatively low. In this paper, we build different neural networks for simple and complex events respectively, which helps to promote the performance of complex event extraction. To avoid redundant information, we design dynamic path planning strategy for argument detection. To take full use of the information between the trigger identification and argument detection subtasks, and reduce the cascading errors, we build a joint event extraction model. Experimental results demonstrate our approach achieves the best F-score on the biomedical benchmark MLEE dataset and outperforms the recent state-of-the-art methods.

Biomedical event argument detection method based on multi-feature fusion and question-answer paradigm

OBJECTIVE

To address the challenges arising from the rapid growth of text data in the biomedical field, including the problems of irrelevant argument interference and deep semantic association neglect in existing event argument detection methods, as well as the difficulty of multiple event extraction. We aim to propose a new event argument detection method that can accurately mine valuable information from biomedical texts through multi-feature fusion and the question-and-answer paradigm, while also addressing the limitations of existing methods.

METHODS

We propose an event argument detection method based on multi-feature fusion and the question-answer paradigm. First, we split each event in the sentence into an independent question-and-answer format to reduce the complexity of detection. Then, in order to reduce the interference of irrelevant arguments, we use syntactic distance and external prior knowledge to find the corresponding argument prior template for each event type. Next, we introduce the multi-feature attention mechanism to fully explore the deep semantic features. Finally, we formulate post-processing methods for predefined event structures to form final biomedical events.

RESULTS

On the MLEE dataset, our model achieved 62.50% in event extraction of F1 scores, which is superior to other advanced event extraction methods.

CONCLUSION

Our method achieves good performance in the event extraction task and provides strong support for the mining of valuable information in biomedical texts.

A biomedical event extraction method based on fine-grained and attention mechanism

BACKGROUND

Biomedical event extraction is a fundamental task in biomedical text mining, which provides inspiration for medicine research and disease prevention. Biomedical events include simple events and complex events. Existing biomedical event extraction methods usually deal with simple events and complex events uniformly, and the performance of complex event extraction is relatively low.

RESULTS

In this paper, we propose a fine-grained Bidirectional Long Short Term Memory method for biomedical event extraction, which designs different argument detection models for simple and complex events respectively. In addition, multi-level attention is designed to improve the performance of complex event extraction, and sentence embeddings are integrated to obtain sentence level information which can resolve the ambiguities for some types of events. Our method achieves state-of-the-art performance on the commonly used dataset Multi-Level Event Extraction.

CONCLUSIONS

The sentence embeddings enrich the global sentence-level information. The fine-grained argument detection model improves the performance of complex biomedical event extraction. Furthermore, the multi-level attention mechanism enhances the interactions among relevant arguments. The experimental results demonstrate the effectiveness of the proposed method for biomedical event extraction.

Conditional Probability Joint Extraction of Nested Biomedical Events: Design of a Unified Extraction Framework Based on Neural Networks (Preprint)

Background

Event extraction is essential for natural language processing. In the biomedical field, the nested event phenomenon (event A as a participating role of event B) makes extracting this event more difficult than extracting a single event. Therefore, the performance of nested biomedical events is always underwhelming. In addition, previous works relied on a pipeline to build an event extraction model, which ignored the dependence between trigger recognition and event argument detection tasks and produced significant cascading errors.

Objective

This study aims to design a unified framework to jointly train biomedical event triggers and arguments and improve the performance of extracting nested biomedical events.

Methods

We proposed an end-to-end joint extraction model that considers the probability distribution of triggers to alleviate cascading errors. Moreover, we integrated the syntactic structure into an attention-based gate graph convolutional network to capture potential interrelations between triggers and related entities, which improved the performance of extracting nested biomedical events.

Results

The experimental results demonstrated that our proposed method achieved the best F1 score on the multilevel event extraction biomedical event extraction corpus and achieved a favorable performance on the biomedical natural language processing shared task 2011 Genia event corpus.

Conclusions

Our conditional probability joint extraction model is good at extracting nested biomedical events because of the joint extraction mechanism and the syntax graph structure. Moreover, as our model did not rely on external knowledge and specific feature engineering, it had a particular generalization performance.

DeepEventMine: end-to-end neural nested event extraction from biomedical texts

MOTIVATION

Recent neural approaches on event extraction from text mainly focus on flat events in general domain, while there are less attempts to detect nested and overlapping events. These existing systems are built on given entities and they depend on external syntactic tools.

RESULTS

We propose an end-to-end neural nested event extraction model named DeepEventMine that extracts multiple overlapping directed acyclic graph structures from a raw sentence. On the top of the bidirectional encoder representations from transformers model, our model detects nested entities and triggers, roles, nested events and their modifications in an end-to-end manner without any syntactic tools. Our DeepEventMine model achieves the new state-of-the-art performance on seven biomedical nested event extraction tasks. Even when gold entities are unavailable, our model can detect events from raw text with promising performance.

AVAILABILITY AND IMPLEMENTATION

Our codes and models to reproduce the results are available at: https://github.com/aistairc/DeepEventMine.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.