AIMC Topic: Comprehension
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Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models.
Proceedings of the National Academy of Sciences of the United States of America
Sep 28, 2020
Language is crucial for human intelligence, but what exactly is its role? We take language to be a part of a system for understanding and communicating about situations. In humans, these abilities emerge gradually from experience and depend on domain...
Understanding the spatial dimension of natural language by measuring the spatial semantic similarity of words through a scalable geospatial context window.
PloS one
Jul 23, 2020
Measuring the semantic similarity between words is important for natural language processing tasks. The traditional models of semantic similarity perform well in most cases, but when dealing with words that involve geographical context, spatial seman...
Clinical Tractor: A Framework for Automatic Natural Language Understanding of Clinical Practice Guidelines.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Mar 4, 2020
Computational representations of the semantic knowledge embedded within clinical practice guidelines (CPGs) may be a significant aid in creating computer interpretable guidelines (CIGs). Formalizing plain text CPGs into CIGs manually is a laborious a...
HarborBot: A Chatbot for Social Needs Screening.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Mar 4, 2020
Accessing patients' social needs is a critical challenge at emergency departments (EDs). However, most EDs do not have extra staff to administer screeners, and without personnel administration, response rates are low especially for low health literac...
Predicting Transition Words Between Sentence for English and Spanish Medical Text.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Mar 4, 2020
Transition words add important information and are useful for increasing text comprehension for readers. Our goal is to automatically detect transition words in the medical domain. We introduce a new dataset for identifying transition words categoriz...
Measuring the diffusion of innovations with paragraph vector topic models.
PloS one
Jan 22, 2020
Measuring the diffusion of innovations from textual data sources besides patent data has not been studied extensively. However, early and accurate indicators of innovation and the recognition of trends in innovation are mandatory to successfully prom...
Quasi-compositional mapping from form to meaning: a neural network-based approach to capturing neural responses during human language comprehension.
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Dec 16, 2019
We argue that natural language can be usefully described as quasi-compositional and we suggest that deep learning-based neural language models bear long-term promise to capture how language conveys meaning. We also note that a successful account of h...
Linguistic generalization and compositionality in modern artificial neural networks.
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Dec 16, 2019
In the last decade, deep artificial neural networks have achieved astounding performance in many natural language-processing tasks. Given the high productivity of language, these models must possess effective generalization abilities. It is widely as...
Evaluating information-theoretic measures of word prediction in naturalistic sentence reading.
Neuropsychologia
Sep 22, 2019
We review information-theoretic measures of cognitive load during sentence processing that have been used to quantify word prediction effort. Two such measures, surprisal and next-word entropy, suffer from shortcomings when employed for a predictive ...
Cortical Tracking of Surprisal during Continuous Speech Comprehension.
Journal of cognitive neuroscience
Sep 3, 2019
Speech comprehension requires rapid online processing of a continuous acoustic signal to extract structure and meaning. Previous studies on sentence comprehension have found neural correlates of the predictability of a word given its context, as well...
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