Spithourakis, GP;
Petersen, SE;
Riedel, S;
(2016)
Clinical Text Prediction with Numerically Grounded Conditional Language Models.
In: Grouin, C and Hamon, T and Névéol, A and Zweigenbaum, P, (eds.)
Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis.
(pp. pp. 6-16).
Association for Computational Linguistics (ACL): Austin, TX, USA.
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Abstract
Assisted text input techniques can save time and effort and improve text quality. In this paper, we investigate how grounded and conditional extensions to standard neural language models can bring improvements in the tasks of word prediction and completion. These extensions incorporate a structured knowledge base and numerical values from the text into the context used to predict the next word. Our automated evaluation on a clinical dataset shows extended models significantly outperform standard models. Our best system uses both conditioning and grounding, because of their orthogonal benefits. For word prediction with a list of 5 suggestions, it improves recall from 25.03% to 71.28% and for word completion it improves keystroke savings from 34.35% to 44.81%, where theoretical bound for this dataset is 58.78%. We also perform a qualitative investigation of how models with lower perplexity occasionally fare better at the tasks. We found that at test time numbers have more influence on the document level than on individual word probabilities.
Type: | Proceedings paper |
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Title: | Clinical Text Prediction with Numerically Grounded Conditional Language Models |
Event: | Seventh International Workshop on Health Text Mining and Information Analysis |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.18653/v1/W16-6102 |
Publisher version: | http://dx.doi.org/10.18653/v1/W16-6102 |
Language: | English |
Additional information: | This is an Open Access paper published under a Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10105096 |
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