Comparing neural models for nested and overlapping biomedical event detection.

Background: Nested and overlapping events are particularly frequent and informative structures in biomedical event extraction. However, state-of-the-art neural models either neglect those structures during learning or use syntactic features and external tools to detect them. To overcome these limitations, this paper presents and compares two neural models: a novel EXhaustive Neural Network (EXNN) and a Search-Based Neural Network (SBNN) for detection of nested and overlapping events.

Annotating and detecting phenotypic information for chronic obstructive pulmonary disease.

Objectives: Chronic obstructive pulmonary disease (COPD) phenotypes cover a range of lung abnormalities. To allow text mining methods to identify pertinent and potentially complex information about these phenotypes from textual data, we have developed a novel annotated corpus, which we use to train a neural network-based named entity recognizer to detect fine-grained COPD phenotypic information.

Materials And Methods: Since COPD phenotype descriptions often mention other concepts within them (proteins, treatments, etc.

Improving reference prioritisation with PICO recognition.

Background: Machine learning can assist with multiple tasks during systematic reviews to facilitate the rapid retrieval of relevant references during screening and to identify and extract information relevant to the study characteristics, which include the PICO elements of patient/population, intervention, comparator, and outcomes. The latter requires techniques for identifying and categorising fragments of text, known as named entity recognition.

Methods: A publicly available corpus of PICO annotations on biomedical abstracts is used to train a named entity recognition model, which is implemented as a recurrent neural network.

An ensemble of neural models for nested adverse drug events and medication extraction with subwords.

Objective: This article describes an ensembling system to automatically extract adverse drug events and drug related entities from clinical narratives, which was developed for the 2018 n2c2 Shared Task Track 2.

Materials And Methods: We designed a neural model to tackle both nested (entities embedded in other entities) and polysemous entities (entities annotated with multiple semantic types) based on MIMIC III discharge summaries. To better represent rare and unknown words in entities, we further tokenized the MIMIC III data set by splitting the words into finer-grained subwords.

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience.

The curation of neuroscience entities is crucial to ongoing efforts in neuroinformatics and computational neuroscience, such as those being deployed in the context of continuing large-scale brain modelling projects. However, manually sifting through thousands of articles for new information about modelled entities is a painstaking and low-reward task. Text mining can be used to help a curator extract relevant information from this literature in a systematic way.

Development and evaluation of task-specific NLP framework in China.

Natural language processing (NLP) has been designed to convert narrative text into structured data. Although some general NLP architectures have been developed, a task-specific NLP framework to facilitate the effective use of data is still a challenge in lexical resource limited regions, such as China. The purpose of this study is to design and develop a task-specific NLP framework to extract targeted information from particular documents by adopting dedicated algorithms on current limited lexical resources.