Inter-sentence relation extraction with document-level graph convolutional neural network
Inter-sentence relation extraction deals with a number of complex semantic relationships in
documents, which require local, non-local, syntactic and semantic dependencies. Existing …
documents, which require local, non-local, syntactic and semantic dependencies. Existing …
[PDF][PDF] How to train good word embeddings for biomedical NLP
The quality of word embeddings depends on the input corpora, model architectures, and
hyper-parameter settings. Using the state-of-the-art neural embedding tool word2vec and …
hyper-parameter settings. Using the state-of-the-art neural embedding tool word2vec and …
[图书][B] Representation learning for natural language processing
This book provides an overview of the recent advances in representation learning theory,
algorithms, and applications for natural language processing (NLP), ranging from word …
algorithms, and applications for natural language processing (NLP), ranging from word …
A tale of two epidemics: Contextual Word2Vec for classifying twitter streams during outbreaks
Unstructured tweet feeds are becoming the source of real-time information for various
events. However, extracting actionable information in real-time from this unstructured text …
events. However, extracting actionable information in real-time from this unstructured text …
Boosting deep learning risk prediction with generative adversarial networks for electronic health records
The rapid growth of Electronic Health Records (EHRs), as well as the accompanied
opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests …
opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests …
[HTML][HTML] Drug-drug interaction extraction from biomedical texts using long short-term memory network
The simultaneous administration of multiple drugs increases the probability of interaction
among them, as one drug may affect the activities of others. This interaction among drugs …
among them, as one drug may affect the activities of others. This interaction among drugs …
BIOSSES: a semantic sentence similarity estimation system for the biomedical domain
Motivation The amount of information available in textual format is rapidly increasing in the
biomedical domain. Therefore, natural language processing (NLP) applications are …
biomedical domain. Therefore, natural language processing (NLP) applications are …
Relation extraction from clinical texts using domain invariant convolutional neural network
In recent years extracting relevant information from biomedical and clinical texts such as
research articles, discharge summaries, or electronic health records have been a subject of …
research articles, discharge summaries, or electronic health records have been a subject of …
Corpus domain effects on distributional semantic modeling of medical terms
SVS Pakhomov, G Finley, R McEwan, Y Wang… - …, 2016 - academic.oup.com
Motivation: Automatically quantifying semantic similarity and relatedness between clinical
terms is an important aspect of text mining from electronic health records, which are …
terms is an important aspect of text mining from electronic health records, which are …
Sentiment analysis and text categorization of cancer medical records with LSTM
Cancer is one among leading diseases, which affects millions of people and families around
the world. Monitoring the mood of such cancer affected people plays a vital part in their …
the world. Monitoring the mood of such cancer affected people plays a vital part in their …