scholarly journals Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning

Database ◽  
2016 ◽  
Vol 2016 ◽  
pp. baw049 ◽  
Author(s):  
Yaoyun Zhang ◽  
Jun Xu ◽  
Hui Chen ◽  
Jingqi Wang ◽  
Yonghui Wu ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Named Entity Recognition ◽  
Feature Learning ◽  
Entity Recognition ◽  
Unsupervised Feature Learning ◽  
Named Entity ◽  
Chemical Named Entity Recognition

Chemical Named Entity Recognition Using Deep Learning Techniques

2021 ◽  
pp. 59-73
Author(s):  
Hema R. ◽  
Ajantha Devi
Keyword(s):  
Deep Learning ◽  
Chemical Elements ◽  
Data Extraction ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Related Data ◽  
Named Entity ◽  
Chemical Structures ◽  
Learning Techniques ◽  
Chemical Named Entity Recognition

Chemical entities can be represented in different forms like chemical names, chemical formulae, and chemical structures. Because of the different classification frameworks for chemical names, the task of distinguishing proof or extraction of chemical elements with less ambiguous is considered a major test. Compound named entity recognition (NER) is the initial phase in any chemical-related data extraction strategy. The majority of the chemical NER is done utilizing dictionary-based, rule-based, and machine learning procedures. Recently, deep learning methods have evolved, and, in this chapter, the authors sketch out the various deep learning techniques applied for chemical NER. First, the authors introduced the fundamental concepts of chemical named entity recognition, the textual contents of chemical documents, and how these chemicals are represented in chemical literature. The chapter concludes with the strengths and weaknesses of the above methods and also the types of the chemical entities extracted.

scholarly journals Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military

2019 ◽  
Vol 11 (8) ◽  
pp. 180
Author(s):  
Fei Liao ◽  
Liangli Ma ◽  
Jingjing Pei ◽  
Linshan Tan
Keyword(s):  
Domain Knowledge ◽  
Short Term Memory ◽  
Contextual Information ◽  
Named Entity Recognition ◽  
Attention Mechanism ◽  
The Self ◽  
Entity Recognition ◽  
Named Entity ◽  
Vector Representations ◽  
The Military

Military named entity recognition (MNER) is one of the key technologies in military information extraction. Traditional methods for the MNER task rely on cumbersome feature engineering and specialized domain knowledge. In order to solve this problem, we propose a method employing a bidirectional long short-term memory (BiLSTM) neural network with a self-attention mechanism to identify the military entities automatically. We obtain distributed vector representations of the military corpus by unsupervised learning and the BiLSTM model combined with the self-attention mechanism is adopted to capture contextual information fully carried by the character vector sequence. The experimental results show that the self-attention mechanism can improve effectively the performance of MNER task. The F-score of the military documents and network military texts identification was 90.15% and 89.34%, respectively, which was better than other models.

scholarly journals Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations

2015 ◽  
Vol 7 (S1) ◽  
Author(s):  
Tsendsuren Munkhdalai ◽  
Meijing Li ◽  
Khuyagbaatar Batsuren ◽  
Hyeon Ah Park ◽  
Nak Hyeon Choi ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Biomedical Named Entity Recognition

A Data-Intensive Approach to Named Entity Recognition Combining Contextual and Intrinsic Indicators

2012 ◽  
Vol 3 (1) ◽  
pp. 55-71 ◽  
Author(s):  
O. Isaac Osesina ◽  
John Talburt
Keyword(s):  
Domain Knowledge ◽  
High Performance ◽  
Business Processes ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Substantial Part ◽  
Data Intensive ◽  
Named Entity ◽  
Unstructured Text ◽  
Wide Availability

Over the past decade, huge volumes of valuable information have become available to organizations. However, the existence of a substantial part of the information in unstructured form makes the automated extraction of business intelligence and decision support information from it difficult. By identifying the entities and their roles within unstructured text in a process known as semantic named entity recognition, unstructured text can be made more readily available for traditional business processes. The authors present a novel NER approach that is independent of the text language and subject domain making it applicable within different organizations. It departs from the natural language and machine learning methods in that it leverages the wide availability of huge amounts of data as well as high-performance computing to provide a data-intensive solution. Also, it does not rely on external resources such as dictionaries and gazettes for the language or domain knowledge.

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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