ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2019
Predicting Transition Words Between Sentence for English and Spanish Medical Text.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- A survey of automated methods for biomedical text simplification.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Creating Logical Flow When Writing Scientific Articles.Journal of Korean medical science · 2021Review
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Authors and funding
4 authors.
Funding
Abstract
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 categorized into 16 different types with occurrences in adjacent sentence pairs in medical texts from English and Spanish Wikipedia (70K and 27K examples, respectively). We provide classification results using a feedforward neural network with word embedding features. Overall, we detect the need for a transition word with 78% accuracy in English and 84% in Spanish. For individual transition word categories, performance varies widely and is not related to either the number of training examples or the number of transition words in the category. The best accuracy in English was for Examplification words (82%) and in Spanish for Contrast words (96%).
Indexed as
Identifiers
32308846PMC7153060What OpenQuestion holds
Registered trials
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