Liyuan Zhou

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Word embeddings – distributed word representations that can be learned from un-labelled data – have been shown to have high utility in many natural language processing applications. In this paper, we perform an extrinsic evaluation of five popular word embedding methods in the context of four sequence labelling tasks: POS-tagging, syntactic chunking, NER(More)
OBJECTIVE We study the use of speech recognition and information extraction to generate drafts of Australian nursing-handover documents. METHODS Speech recognition correctness and clinicians' preferences were evaluated using 15 recorder-microphone combinations, six documents, three speakers, Dragon Medical 11, and five survey/interview participants.(More)
Cascaded speech recognition (SR) and information extraction (IE) could support the best practice for clinical handover and release clinicians' time from writing documents to patient interaction and education. However, high requirements for processing correctness evoke methodological challenges and hence, processing correctness needs to be carefully(More)
During clinical handover, clinicians exchange information about the patients and the state of clinical management. To improve care safety and quality, both handover and its documentation have been standardized. Speech recognition and entity extraction provide a way to help health service providers to follow these standards by implementing the handover(More)
Xieyou9308 is a certified super hybrid rice cultivar with a high grain yield. To investigate its underlying genetic basis of high yield potential, a recombinant inbred line (RIL) population derived from the cross between the maintainer line XieqingzaoB (XQZB) and the restorer line Zhonghui9308 (ZH9308) was constructed for identification of quantitative(More)
In named entity recognition, we often don't have a large in-domain training corpus or a knowledge base with adequate coverage to train a model directly. In this paper, we propose a method where, given training data in a related domain with similar (but not identical) named entity (NE) types and a small amount of in-domain training data, we use transfer(More)
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