AIMC Topic: Language

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How to automatically turn patient experience free-text responses into actionable insights: a natural language programming (NLP) approach.

BMC medical informatics and decision making
BACKGROUND: Patient experience surveys often include free-text responses. Analysis of these responses is time-consuming and often underutilized. This study examined whether Natural Language Processing (NLP) techniques could provide a data-driven, hos...

Summarization of biomedical articles using domain-specific word embeddings and graph ranking.

Journal of biomedical informatics
Text summarization tools can help biomedical researchers and clinicians reduce the time and effort needed for acquiring important information from numerous documents. It has been shown that the input text can be modeled as a graph, and important sent...

Analog neuron hierarchy.

Neural networks : the official journal of the International Neural Network Society
In order to refine the analysis of the computational power of discrete-time recurrent neural networks (NNs) between the binary-state NNs which are equivalent to finite automata (level 3 in the Chomsky hierarchy), and the analog-state NNs with rationa...

The influence of preprocessing on text classification using a bag-of-words representation.

PloS one
Text classification (TC) is the task of automatically assigning documents to a fixed number of categories. TC is an important component in many text applications. Many of these applications perform preprocessing. There are different types of text pre...

Distributed representation and one-hot representation fusion with gated network for clinical semantic textual similarity.

BMC medical informatics and decision making
BACKGROUND: Semantic textual similarity (STS) is a fundamental natural language processing (NLP) task which can be widely used in many NLP applications such as Question Answer (QA), Information Retrieval (IR), etc. It is a typical regression problem,...

Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records.

BMC medical informatics and decision making
BACKGROUND: Capturing sentence semantics plays a vital role in a range of text mining applications. Despite continuous efforts on the development of related datasets and models in the general domain, both datasets and models are limited in biomedical...

The Language of Innovation.

PloS one
Predicting innovation is a peculiar problem in data science. Following its definition, an innovation is always a never-seen-before event, leaving no room for traditional supervised learning approaches. Here we propose a strategy to address the proble...

Natural Language Generation Model for Mammography Reports Simulation.

IEEE journal of biomedical and health informatics
Extending the size of labeled corpora of medical reports is a major step towards a successful training of machine learning algorithms. Simulating new text reports is a key solution for reports augmentation, which extends the cohort size. However, tex...

Analyzing and learning the language for different types of harassment.

PloS one
THIS ARTICLE USES WORDS OR LANGUAGE THAT IS CONSIDERED PROFANE, VULGAR, OR OFFENSIVE BY SOME READERS. The presence of a significant amount of harassment in user-generated content and its negative impact calls for robust automatic detection approaches...

Modeling coherence by ordering paragraphs using pointer networks.

Neural networks : the official journal of the International Neural Network Society
Coherence is a distinctive feature in well-written documents. One method to study coherence is to analyze how sentences are ordered in a document. In Multi-document Summarization, sentences from different sources need to be ordered. Cluster-based ord...