scholarly journals UTU: Adapting Biomedical Event Extraction System to Disorder Attribute Detection

2015 ◽  
Author(s):  
Kai Hakala
Keyword(s):  
Event Extraction ◽  
Extraction System ◽  
Biomedical Event Extraction

scholarly journals EVENT EXTRACTION WITH COMPLEX EVENT CLASSIFICATION USING RICH FEATURES

2010 ◽  
Vol 08 (01) ◽  
pp. 131-146 ◽  
Author(s):  
MAKOTO MIWA ◽  
RUNE SÆTRE ◽  
JIN-DONG KIM ◽  
JUN'ICHI TSUJII
Keyword(s):  
Language Processing ◽  
High Performance ◽  
Classification Problem ◽  
Event Extraction ◽  
Extraction System ◽  
Binary Relations ◽  
Shared Task ◽  
Large Numbers ◽  
Biomedical Event Extraction ◽  
Complex Events

Biomedical Natural Language Processing (BioNLP) attempts to capture biomedical phenomena from texts by extracting relations between biomedical entities (i.e. proteins and genes). Traditionally, only binary relations have been extracted from large numbers of published papers. Recently, more complex relations (biomolecular events) have also been extracted. Such events may include several entities or other relations. To evaluate the performance of the text mining systems, several shared task challenges have been arranged for the BioNLP community. With a common and consistent task setting, the BioNLP'09 shared task evaluated complex biomolecular events such as binding and regulation.Finding these events automatically is important in order to improve biomedical event extraction systems. In the present paper, we propose an automatic event extraction system, which contains a model for complex events, by solving a classification problem with rich features. The main contributions of the present paper are: (1) the proposal of an effective bio-event detection method using machine learning, (2) provision of a high-performance event extraction system, and (3) the execution of a quantitative error analysis. The proposed complex (binding and regulation) event detector outperforms the best system from the BioNLP'09 shared task challenge.

scholarly journals Biomedical Event Extraction as Sequence Labeling

2020 ◽  
Author(s):  
Alan Ramponi ◽  
Rob van der Goot ◽  
Rosario Lombardo ◽  
Barbara Plank
Keyword(s):  
Event Extraction ◽  
Sequence Labeling ◽  
Biomedical Event Extraction

scholarly journals A Novel Feature Selection Strategy for Enhanced Biomedical Event Extraction Using the Turku System

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jingbo Xia ◽  
Alex Chengyu Fang ◽  
Xing Zhang
Keyword(s):  
Feature Selection ◽  
Search Strategy ◽  
Evaluation Criteria ◽  
Event Extraction ◽  
High Dimensional ◽  
Selection Strategy ◽  
Effect Evaluation ◽  
Greedy Search ◽  
Single Feature ◽  
Biomedical Event Extraction

Feature selection is of paramount importance for text-mining classifiers with high-dimensional features. The Turku Event Extraction System (TEES) is the best performing tool in the GENIA BioNLP 2009/2011 shared tasks, which relies heavily on high-dimensional features. This paper describes research which, based on an implementation of an accumulated effect evaluation (AEE) algorithm applying the greedy search strategy, analyses the contribution of every single feature class in TEES with a view to identify important features and modify the feature set accordingly. With an updated feature set, a new system is acquired with enhanced performance which achieves an increasedF-score of 53.27% up from 51.21% for Task 1 under strict evaluation criteria and 57.24% according to the approximate span and recursive criterion.

scholarly journals Semi-supervised method for biomedical event extraction

2013 ◽  
Vol 11 (Suppl 1) ◽  
pp. S17 ◽  
Author(s):  
Jian Wang ◽  
Qian Xu ◽  
Hongfei Lin ◽  
Zhihao Yang ◽  
Yanpeng Li
Keyword(s):  
Event Extraction ◽  
Biomedical Event Extraction

scholarly journals Injecting Event Knowledge into Pre-Trained Language Models for Event Extraction

2020 ◽  
Author(s):  
Zining Yang ◽  
Siyu Zhan ◽  
Mengshu Hou ◽  
Xiaoyang Zeng ◽  
Hao Zhu
Keyword(s):  
Language Model ◽  
Empirical Evaluation ◽  
Event Extraction ◽  
Training Data ◽  
Language Models ◽  
Extraction System ◽  
Training Dataset ◽  
Great Success ◽  
Event Knowledge ◽  
Event Trigger

The recent pre-trained language model has made great success in many NLP tasks. In this paper, we propose an event extraction system based on the novel pre-trained language model BERT to extract both event trigger and argument. As a deep-learningbased method, the size of the training dataset has a crucial impact on performance. To address the lacking training data problem for event extraction, we further train the pretrained language model with a carefully constructed in-domain corpus to inject event knowledge to our event extraction system with minimal efforts. Empirical evaluation on the ACE2005 dataset shows that injecting event knowledge can significantly improve the performance of event extraction.

scholarly journals Context awareness and embedding for biomedical event extraction

2019 ◽  
Author(s):  
Shankai Yan ◽  
Ka-Chun Wong
Keyword(s):  
Short Term Memory ◽  
Event Extraction ◽  
Supplementary Information ◽  
Short Term ◽  
Biomedical Knowledge ◽  
Biomedical Event Extraction ◽  
Training Event ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Massive Information

Abstract Motivation Biomedical event extraction is fundamental for information extraction in molecular biology and biomedical research. The detected events form the central basis for comprehensive biomedical knowledge fusion, facilitating the digestion of massive information influx from the literature. Limited by the event context, the existing event detection models are mostly applicable for a single task. A general and scalable computational model is desiderated for biomedical knowledge management. Results We consider and propose a bottom-up detection framework to identify the events from recognized arguments. To capture the relations between the arguments, we trained a bidirectional long short-term memory network to model their context embedding. Leveraging the compositional attributes, we further derived the candidate samples for training event classifiers. We built our models on the datasets from BioNLP Shared Task for evaluations. Our method achieved the average F-scores of 0.81 and 0.92 on BioNLPST-BGI and BioNLPST-BB datasets, respectively. Comparing with seven state-of-the-art methods, our method nearly doubled the existing F-score performance (0.92 versus 0.56) on the BioNLPST-BB dataset. Case studies were conducted to reveal the underlying reasons. Availability and implementation https://github.com/cskyan/evntextrc. Supplementary information Supplementary data are available at Bioinformatics online.

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