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 Ying Xu

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Average citations per article0.00
Citation Count0
Publication count3
Publication years2010-2015
Available for download2
Average downloads per article194.50
Downloads (cumulative)389
Downloads (12 Months)93
Downloads (6 Weeks)11
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1 published by ACM
July 2015 ACM Transactions on Intelligent Systems and Technology (TIST) - Regular Papers and Special Section on Intelligent Healthcare Informatics: Volume 6 Issue 4, August 2015
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 7,   Downloads (12 Months): 69,   Downloads (Overall): 193

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We explore methods for effectively extracting information from clinical narratives that are captured in a public health consulting phone service called HealthLink. Our research investigates the application of state-of-the-art natural language processing and machine learning to clinical narratives to extract information of interest. The currently available data consist of dialogues ...
Keywords: named entity recognition, biomedical text mining, Tele-health mining, effective information retrieval

2
November 2012 JSAI-isAI'12: Proceedings of the 2012 international conference on New Frontiers in Artificial Intelligence
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 0

We describe a method for extractive summarization of legal judgments using our own graph-based summarization algorithm. In contrast to the connected and undirected graphs of previous work, we construct directed and disconnected graphs (a set of connected graphs) for each document, where each connected graph indicates a cluster that shares ...
Keywords: summarization, information extraction, legal case, graph representation

3 published by ACM
October 2010 AND '10: Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 4,   Downloads (12 Months): 24,   Downloads (Overall): 196

Full text available: PDFPDF
'For the past 4-5 years, we've been investigating a variety of computational methods for extracting linguistic structures from relatively large language corpora. These include the use of well-known standard labeled language resources such as those from the Linguistic Data Consortium, as well as a spectrum of unlabeled resources, including the ...
Keywords: natural language processing machine learning



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