AIMC Topic: Language

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Do You Need Embeddings Trained on a Massive Specialized Corpus for Your Clinical Natural Language Processing Task?

Studies in health technology and informatics
We explore the impact of data source on word representations for different NLP tasks in the clinical domain in French (natural language understanding and text classification). We compared word embeddings (Fasttext) and language models (ELMo), learned...

Extracting Symptom Names and Disease-Symptom Relationships from Web Texts Using a Multi-Column Convolutional Neural Network.

Studies in health technology and informatics
We propose a method to create large-scale Japanese medical dictionaries that include symptom names and information about the relationship between a disease and its symptoms using a large web archive that includes large amounts of text written by non-...

Identifying Diabetes in Clinical Notes in Hebrew: A Novel Text Classification Approach Based on Word Embedding.

Studies in health technology and informatics
NimbleMiner is a word embedding-based, language-agnostic natural language processing system for clinical text classification. Previously, NimbleMiner was applied in English and this study applied NimbleMiner on a large sample of inpatient clinical no...

Recurrent Deep Network Models for Clinical NLP Tasks: Use Case with Sentence Boundary Disambiguation.

Studies in health technology and informatics
Although a number of foundational natural language processing (NLP) tasks like text segmentation are considered a simple problem in the general English domain dominated by well-formed text, complexities of clinical documentation lead to poor performa...

Learning Portuguese Clinical Word Embeddings: A Multi-Specialty and Multi-Institutional Corpus of Clinical Narratives Supporting a Downstream Biomedical Task.

Studies in health technology and informatics
In this paper, we trained a set of Portuguese clinical word embedding models of different granularities from multi-specialty and multi-institutional clinical narrative datasets. Then, we assessed their impact on a downstream biomedical NLP task of Ur...

Word Embedding for French Natural Language in Healthcare: A Comparative Study.

Studies in health technology and informatics
Structuring raw medical documents with ontology mapping is now the next step for medical intelligence. Deep learning models take as input mathematically embedded information, such as encoded texts. To do so, word embedding methods can represent every...

Named Entity Recognition and Classification for Medical Prospectuses.

Studies in health technology and informatics
Structuring and processing natural language is a growing challenge in the medical field. Researchers are looking for new ways to extract knowledge to create databases and applications to help doctors treat patients and minimize medical errors. A very...

SLES: A Novel CNN-based Method for Sensor Reduction in P300 Speller.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
A Brain Computer Interface (BCI) character speller allows human-beings to directly spell characters using eye-gazes, thereby building communication between the human brain and a computer. Current popular BCI character speller systems employ a large n...

Natural language generation for social robotics: opportunities and challenges.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
In the increasingly popular and diverse research area of social robotics, the primary goal is to develop robot agents that exhibit socially intelligent behaviour while interacting in a face-to-face context with human partners. An important aspect of ...

Medical assertion classification in Chinese EMRs using attention enhanced neural network.

Mathematical biosciences and engineering : MBE
Electronic medical records (EMRs), such as hospital discharge summaries, contain a wealth of information only expressed in natural language. Automated methods for extracting information from these records must be able to recognize medical concepts in...