scholarly journals A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events

2020 ◽  
Author(s):  
Susmitha Wunnava ◽  
Xiao Qin ◽  
Tabassum Kakar ◽  
Xiangnan Kong ◽  
Elke Rundensteiner
Keyword(s):  
Adverse Drug Events ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Attention Network ◽  
Named Entity ◽  
Sentence Classification

scholarly journals A Probability based Classification of Named Entities for Malayalam Language combining Word, Part of Speech and Lexicalized features

2019 ◽  
Vol 8 (2) ◽  
pp. 839-842
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Supervised Machine Learning ◽  
Named Entities ◽  
Named Entity ◽  
Domain Specific ◽  
Part Of Speech ◽  
Classification Probability ◽  
Malayalam Language

Named Entity Recognition is the process wherein named entities which are designators of a sentence are identified. Designators of a sentence are domain specific. The proposed system identifies named entities in Malayalam language belonging to tourism domain which generally includes names of persons, places, organizations, dates etc. The system uses word, part of speech and lexicalized features to find the probability of a word belonging to a named entity category and to do the appropriate classification. Probability is calculated based on supervised machine learning using word and part of speech features present in a tagged training corpus and using certain rules applied based on lexicalized features.

scholarly journals Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach

2019 ◽  
Vol 16 (19) ◽  
pp. 3628 ◽  
Author(s):  
Erdenebileg Batbaatar ◽  
Keun Ho Ryu
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Adverse Drug Events ◽  
Viterbi Algorithm ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Health Related

Named Entity Recognition (NER) in the healthcare domain involves identifying and categorizing disease, drugs, and symptoms for biosurveillance, extracting their related properties and activities, and identifying adverse drug events appearing in texts. These tasks are important challenges in healthcare. Analyzing user messages in social media networks such as Twitter can provide opportunities to detect and manage public health events. Twitter provides a broad range of short messages that contain interesting information for information extraction. In this paper, we present a Health-Related Named Entity Recognition (HNER) task using healthcare-domain ontology that can recognize health-related entities from large numbers of user messages from Twitter. For this task, we employ a deep learning architecture which is based on a recurrent neural network (RNN) with little feature engineering. To achieve our goal, we collected a large number of Twitter messages containing health-related information, and detected biomedical entities from the Unified Medical Language System (UMLS). A bidirectional long short-term memory (BiLSTM) model learned rich context information, and a convolutional neural network (CNN) was used to produce character-level features. The conditional random field (CRF) model predicted a sequence of labels that corresponded to a sequence of inputs, and the Viterbi algorithm was used to detect health-related entities from Twitter messages. We provide comprehensive results giving valuable insights for identifying medical entities in Twitter for various applications. The BiLSTM-CRF model achieved a precision of 93.99%, recall of 73.31%, and F1-score of 81.77% for disease or syndrome HNER; a precision of 90.83%, recall of 81.98%, and F1-score of 87.52% for sign or symptom HNER; and a precision of 94.85%, recall of 73.47%, and F1-score of 84.51% for pharmacologic substance named entities. The ontology-based manual annotation results show that it is possible to perform high-quality annotation despite the complexity of medical terminology and the lack of context in tweets.

scholarly journals Improving classification of low-resource COVID-19 literature by using Named Entity Recognition

2021 ◽  
Vol 19 (3) ◽  
pp. e22
Author(s):  
Oscar Lithgow-Serrano ◽  
Joseph Cornelius ◽  
Vani Kanjirangat ◽  
Carlos-Francisco Méndez-Cruz ◽  
Fabio Rinaldi
Keyword(s):  
Classification Scheme ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Biological Databases ◽  
Proof Of Concept ◽  
Baseline Model ◽  
Named Entities ◽  
Named Entity ◽  
Automatic Document Classification

Automatic document classification for highly interrelated classes is a demanding task that becomes more challenging when there is little labeled data for training. Such is the case of the coronavirus disease 2019 (COVID-19) Clinical repository—a repository of classified and translated academic articles related to COVID-19 and relevant to the clinical practice—where a 3-way classification scheme is being applied to COVID-19 literature. During the 7th Biomedical Linked Annotation Hackathon (BLAH7) hackathon, we performed experiments to explore the use of named-entity-recognition (NER) to improve the classification. We processed the literature with OntoGene’s Biomedical Entity Recogniser (OGER) and used the resulting identified Named Entities (NE) and their links to major biological databases as extra input features for the classifier. We compared the results with a baseline model without the OGER extracted features. In these proof-of-concept experiments, we observed a clear gain on COVID-19 literature classification. In particular, NE’s origin was useful to classify document types and NE’s type for clinical specialties. Due to the limitations of the small dataset, we can only conclude that our results suggests that NER would benefit this classification task. In order to accurately estimate this benefit, further experiments with a larger dataset would be needed.

scholarly journals Automatic Arabic Named Entity Extraction and Classification for Information Retrieval

2020 ◽  
Vol 9 (6) ◽  
pp. 1-22
Author(s):  
Omar ASBAYOU
Keyword(s):  
Information Retrieval ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Entity Extraction ◽  
Rule Based ◽  
Named Entity ◽  
Named Entity Extraction ◽  
Name Entity ◽  
System Output

This article tries to explain our rule-based Arabic Named Entity recognition (NER) and classification system. It is based on lists of classified proper names (PN) and particularly on syntactico-semantic patterns resulting in fine classification of Arabic NE. These patterns use syntactico-semantic combination of morpho-syntactic and syntactic entities. It also uses lexical classification of trigger words and NE extensions. These linguistic data are essential not only to name entity extraction but also to the taxonomic classification and to determining the NE frontiers. Our method is also based on the contextualisation and on the notion of NE class attributes and values. Inspired from X-bar theory and immediate constituents, we built a rule-based NER system composed of five levels of syntactico-semantic combination. We also show how the fine NE annotations in our system output (XML database) is exploited in information retrieval and information extraction.

scholarly journals Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting

2019 ◽  
Vol 27 (1) ◽  
pp. 65-72 ◽  
Author(s):  
Xi Yang ◽  
Jiang Bian ◽  
Ruogu Fang ◽  
Ragnhildur I Bjarnadottir ◽  
William R Hogan ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Adverse Drug Events ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Gradient Boosting ◽  
Support Vector ◽  
Named Entity ◽  
Vector Machines ◽  
Relation Classification

Abstract Objective To develop a natural language processing system that identifies relations of medications with adverse drug events from clinical narratives. This project is part of the 2018 n2c2 challenge. Materials and Methods We developed a novel clinical named entity recognition method based on an recurrent convolutional neural network and compared it to a recurrent neural network implemented using the long-short term memory architecture, explored methods to integrate medical knowledge as embedding layers in neural networks, and investigated 3 machine learning models, including support vector machines, random forests and gradient boosting for relation classification. The performance of our system was evaluated using annotated data and scripts provided by the 2018 n2c2 organizers. Results Our system was among the top ranked. Our best model submitted during this challenge (based on recurrent neural networks and support vector machines) achieved lenient F1 scores of 0.9287 for concept extraction (ranked third), 0.9459 for relation classification (ranked fourth), and 0.8778 for the end-to-end relation extraction (ranked second). We developed a novel named entity recognition model based on a recurrent convolutional neural network and further investigated gradient boosting for relation classification. The new methods improved the lenient F1 scores of the 3 subtasks to 0.9292, 0.9633, and 0.8880, respectively, which are comparable to the best performance reported in this challenge. Conclusion This study demonstrated the feasibility of using machine learning methods to extract the relations of medications with adverse drug events from clinical narratives.

scholarly journals Arabic Location Name Annotations and Applications

2020 ◽  
Author(s):  
Omar ASBAYOU
Keyword(s):  
Classification System ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Syntactic Analysis ◽  
Word Sense ◽  
Semantic Classification ◽  
Named Entity ◽  
Semantic Data ◽  
Linguistic Approach

This paper show how location named entity (LNE) extraction and annotation, which makes part of our named entity recognition (NER) systems, is an important task in managing the great amount of data. In this paper, we try to explain our linguistic approach in our rule-based LNE recognition and classification system based on syntactico-semantic patterns. To reach good results, we have taken into account morpho-syntactic information provided by morpho-syntactic analysis based on DIINAR database, and syntactico-semantic classification of both location name trigger words (TW) and extensions. Formally, different trigger word sense implies different syntactic entity structures. We also show the semantic data that our LNE recognition and classification system can provide to both information extraction (IE) and information retrieval(IR).The XML database output of the LNE system constituted an important resource for IE and IR. Future project will improve this processing output in order to exploit it in computerassisted Translation (CAT).

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