Parallel sequence tagging for concept recognition

• URL: http://arxiv.org/abs/2003.07424v2
• Date: Sat, 8 Aug 2020 07:54:17 GMT
• Title: Parallel sequence tagging for concept recognition
• Authors: Lenz Furrer (1 and 3), Joseph Cornelius (1), Fabio Rinaldi (1, 2, and 3) ((1) University of Zurich, Switzerland, (2) Dalle Molle Institute for Artificial Intelligence Research (IDSIA), Switzerland, (3) Swiss Institute of Bioinformatics, Switzerland)
• Abstract summary: Named Entity Recognition (NER) and Normalisation (NEN) are core components of any text-mining system for biomedical texts. In a traditional concept-recognition pipeline, these tasks are combined in a serial way, which is inherently prone to error propagation from NER to NEN. We propose a parallel architecture, where both NER and NEN are modeled as a sequence-labeling task, operating directly on the source text.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Named Entity Recognition (NER) and Normalisation (NEN) are core components of any text-mining system for biomedical texts. In a traditional concept-recognition pipeline, these tasks are combined in a serial way, which is inherently prone to error propagation from NER to NEN. We propose a parallel architecture, where both NER and NEN are modeled as a sequence-labeling task, operating directly on the source text. We examine different harmonisation strategies for merging the predictions of the two classifiers into a single output sequence. Results: We test our approach on the recent Version 4 of the CRAFT corpus. In all 20 annotation sets of the concept-annotation task, our system outperforms the pipeline system reported as a baseline in the CRAFT shared task 2019. Conclusions: Our analysis shows that the strengths of the two classifiers can be combined in a fruitful way. However, prediction harmonisation requires individual calibration on a development set for each annotation set. This allows achieving a good trade-off between established knowledge (training set) and novel information (unseen concepts). Availability and Implementation: Source code freely available for download at https://github.com/OntoGene/craft-st. Supplementary data are available at arXiv online.

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