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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 5 of 5 1 Modelling Instance-Level Annotator Reliability for Natural Language Labelling Tasks ... Li, Maolin; Myrman, Arvid Fahlström; Mu, Tingting. - : arXiv, 2019 BASE Show details 2 Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network ... Sahu, Sunil Kumar; Christopoulou, Fenia; Miwa, Makoto. - : arXiv, 2019 BASE Show details 3 Hypothesis, analysis and synthesis: it’s all Greek to me! ... Iliopoulos, Ioannis; Ananiadou, Sophia; Danchin, Antoine. - : figshare, 2019 BASE Show details 4 Improving reference prioritisation with PICO recognition Brockmeier, Austin J.; Ju, Meizhi; Przybyła, Piotr. - : BioMed Central, 2019 BASE Show details 5 Improving clinical named entity recognition in Chinese using the graphical and phonetic feature Wang, Yifei; Ananiadou, Sophia; Tsujii, Jun’ichi. - : BioMed Central, 2019 Abstract: BACKGROUND: Clinical Named Entity Recognition is to find the name of diseases, body parts and other related terms from the given text. Because Chinese language is quite different with English language, the machine cannot simply get the graphical and phonetic information form Chinese characters. The method for Chinese should be different from that for English. Chinese characters present abundant information with the graphical features, recent research on Chinese word embedding tries to use graphical information as subword. This paper uses both graphical and phonetic features to improve Chinese Clinical Named Entity Recognition based on the presence of phono-semantic characters. METHODS: This paper proposed three different embedding models and tested them on the annotated data. The data have been divided into two sections for exploring the effect of the proportion of phono-semantic characters. RESULTS: The model using primary radical and pinyin can improve Clinical Named Entity Recognition in Chinese and get the F-measure of 0.712. More phono-semantic characters does not give a better result. CONCLUSIONS: The paper proves that the use of the combination of graphical and phonetic features can improve the Clinical Named Entity Recognition in Chinese. Keyword: Research URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927114/ https://doi.org/10.1186/s12911-019-0980-z http://www.ncbi.nlm.nih.gov/pubmed/31865903 BASE Hide details

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