scholarly journals NAMED ENTITY RECOGNITION FROM BIOMEDICAL TEXT -AN INFORMATION EXTRACTION TASK

2016 ◽  
Vol 6 (4) ◽  
pp. 1303-1307 ◽  
Author(s):  
Kanya N ◽  
◽  
Ravi T ◽  
Keyword(s):  
Information Extraction ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Biomedical Text ◽  
Named Entity

scholarly journals Exploring the Adaptation of Recurrent Neural Network Approaches for Extracting Drug–Drug Interactions from Biomedical Text

2021 ◽  
Vol 11 (4) ◽  
pp. 267-273
Author(s):  
Wen-Juan Hou ◽  
◽  
Bamfa Ceesay
Keyword(s):  
Text Processing ◽  
Named Entity Recognition ◽  
Event Extraction ◽  
Entity Recognition ◽  
Biomedical Text ◽  
Automatic Extraction ◽  
Named Entity ◽  
Structured Information ◽  
Network Approaches ◽  
Form Information

Information extraction (IE) is the process of automatically identifying structured information from unstructured or partially structured text. IE processes can involve several activities, such as named entity recognition, event extraction, relationship discovery, and document classification, with the overall goal of translating text into a more structured form. Information on the changes in the effect of a drug, when taken in combination with a second drug, is known as drug–drug interaction (DDI). DDIs can delay, decrease, or enhance absorption of drugs and thus decrease or increase their efficacy or cause adverse effects. Recent research trends have shown several adaptation of recurrent neural networks (RNNs) from text. In this study, we highlight significant challenges of using RNNs in biomedical text processing and propose automatic extraction of DDIs aiming at overcoming some challenges. Our results show that the system is competitive against other systems for the task of extracting DDIs.

scholarly journals A Neural Named Entity Recognition and Multi-Type Normalization Tool for Biomedical Text Mining

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 73729-73740 ◽  
Author(s):  
Donghyeon Kim ◽  
Jinhyuk Lee ◽  
Chan Ho So ◽  
Hwisang Jeon ◽  
Minbyul Jeong ◽  
...  
Keyword(s):  
Text Mining ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Biomedical Text ◽  
Biomedical Text Mining ◽  
Named Entity

Person Named Entity Recognition in Balinese

2021 ◽  
Vol 10 (1) ◽  
pp. 99
Author(s):  
Kenny Kurniadi ◽  
Ngurah Agus Sanjaya ER
Keyword(s):  
Information Extraction ◽  
Named Entity Recognition ◽  
Morphological Structure ◽  
Entity Recognition ◽  
Linguistic Meaning ◽  
Rule Based ◽  
Named Entity

Named Entity Recognition (NER) is part of information extraction whose task is to classify text which is categorized into several classes such as names of people (figures), organizations, and locations. In this study, the authors propose making a NER identify the names of characters in Balinese language documents. This study will use a rule-based method (rule-based). Rules are build based on the morphological structure and linguistic meaning of Balinese names. The research conducted, that the system has an accuracy of 67.41%, precision of 83.42%, recall of 77.83%, and F-Score of 80.53%.

scholarly journals Identifying interactions between chemical entities in biomedical text

2014 ◽  
Vol 11 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Andre Lamurias ◽  
João D. Ferreira ◽  
Francisco M. Couto
Keyword(s):  
Named Entity Recognition ◽  
Relation Extraction ◽  
Ensemble Classifier ◽  
Entity Recognition ◽  
Support Vector ◽  
Biomedical Text ◽  
Web Tool ◽  
Named Entity ◽  
Vector Machines ◽  
Chemical Named Entity Recognition

Summary Interactions between chemical compounds described in biomedical text can be of great importance to drug discovery and design, as well as pharmacovigilance. We developed a novel system, “Identifying Interactions between Chemical Entities” (IICE), to identify chemical interactions described in text. Kernel-based Support Vector Machines first identify the interactions and then an ensemble classifier validates and classifies the type of each interaction. This relation extraction module was evaluated with the corpus released for the DDI Extraction task of SemEval 2013, obtaining results comparable to stateof- the-art methods for this type of task. We integrated this module with our chemical named entity recognition module and made the whole system available as a web tool at www.lasige.di.fc.ul.pt/webtools/iice.

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