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Cross-linguistically, morphological material that expresses comparison (e.g. more) appears to be colexified with aspectual (“phasal”) adverbs that, under negation, encode the termination of some eventuality (CESSATIVEs, e.g. *(not)...anymore). Using data drawn from the Diyari language of central Australia, we propose a diachronic trajectory for the lexical item marla ‘very, truly’. This word first developed a comparative semantics and, subsequently, a cessative reading restricted to negative polar contexts. This proposal moves us towards a lexical entry that permits for the unification of comparative and aspectual readings for items which exhibit this polysemy and—on the basis of robust pragmatic principles— predicts their polarity-sensitive distribution cross-linguistically.
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Understanding the Polarity of Events in the Biomedical Literature: Deep Learning vs. Linguistically-informed Methods
An important task in the machine reading of biochemical events expressed in biomedical texts is correctly reading the polarity, i.e., attributing whether the biochemical event is a promotion or an inhibition. Here we present a novel dataset for studying polarity attribution accuracy. We use this dataset to train and evaluate several deep learning models for polarity identification, and compare these to a linguistically-informed model. The best performing deep learning architecture achieves 0.968 average F1 performance in a five-fold cross-validation study, a considerable improvement over the linguistically informed model average F1 of 0.862.
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- Award ID(s):
- 1740858
- PAR ID:
- 10111684
- Date Published:
- Journal Name:
- Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications
- Page Range / eLocation ID:
- 21 to 30
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/W19-2603
