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Bidirectional LSTM for Named Entity Recognition in Twitter Messages
In this paper, we present our approach for named entity recognition in Twitter messages that we used in our participation in the Named Entity Recognition in Twitter shared task at the COLING 2016 Workshop on Noisy User-generated text (WNUT). The main challenge that we aim to tackle in our participation is the short, noisy and colloquial nature of tweets, which makes named entity recognition in Twitter messages a challenging task. In particular, we investigate an approach for dealing with this problem by enabling bidirectional long short-term memory (LSTM) to automatically learn orthographic features without requiring feature engineering. In comparison with other systems participating in the shared task, our system achieved the most effective performance on both the ‘segmentation and categorisation’ and the ‘segmentation only’ sub-tasks
DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature
We consider the problem of Named Entity Recognition (NER) on biomedical
scientific literature, and more specifically the genomic variants recognition
in this work. Significant success has been achieved for NER on canonical tasks
in recent years where large data sets are generally available. However, it
remains a challenging problem on many domain-specific areas, especially the
domains where only small gold annotations can be obtained. In addition, genomic
variant entities exhibit diverse linguistic heterogeneity, differing much from
those that have been characterized in existing canonical NER tasks. The
state-of-the-art machine learning approaches in such tasks heavily rely on
arduous feature engineering to characterize those unique patterns. In this
work, we present the first successful end-to-end deep learning approach to
bridge the gap between generic NER algorithms and low-resource applications
through genomic variants recognition. Our proposed model can result in
promising performance without any hand-crafted features or post-processing
rules. Our extensive experiments and results may shed light on other similar
low-resource NER applications.Comment: accepted by AAAI 202
A two-stage deep learning approach for extracting entities and relationships from medical texts
This Work Presents A Two-Stage Deep Learning System For Named Entity Recognition (Ner) And Relation Extraction (Re) From Medical Texts. These Tasks Are A Crucial Step To Many Natural Language Understanding Applications In The Biomedical Domain. Automatic Medical Coding Of Electronic Medical Records, Automated Summarizing Of Patient Records, Automatic Cohort Identification For Clinical Studies, Text Simplification Of Health Documents For Patients, Early Detection Of Adverse Drug Reactions Or Automatic Identification Of Risk Factors Are Only A Few Examples Of The Many Possible Opportunities That The Text Analysis Can Offer In The Clinical Domain. In This Work, Our Efforts Are Primarily Directed Towards The Improvement Of The Pharmacovigilance Process By The Automatic Detection Of Drug-Drug Interactions (Ddi) From Texts. Moreover, We Deal With The Semantic Analysis Of Texts Containing Health Information For Patients. Our Two-Stage Approach Is Based On Deep Learning Architectures. Concretely, Ner Is Performed Combining A Bidirectional Long Short-Term Memory (Bi-Lstm) And A Conditional Random Field (Crf), While Re Applies A Convolutional Neural Network (Cnn). Since Our Approach Uses Very Few Language Resources, Only The Pre-Trained Word Embeddings, And Does Not Exploit Any Domain Resources (Such As Dictionaries Or Ontologies), This Can Be Easily Expandable To Support Other Languages And Clinical Applications That Require The Exploitation Of Semantic Information (Concepts And Relationships) From Texts...This work was supported by the Research Program of the Ministry of Economy and Competitiveness - Government of Spain, (DeepEMR project TIN2017-87548-C2-1-R)
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