scholarly journals Using distributional similarity to organise biomedical terminology

Author(s):  
Julie Weeds ◽  
James Dowdall ◽  
Gerold Schneider ◽  
Bill Keller ◽  
David J. Weir
Terminology ◽  
2005 ◽  
Vol 11 (1) ◽  
pp. 107-141 ◽  
Author(s):  
Julie Weeds ◽  
James Dowdall ◽  
Gerold Schneider ◽  
Bill Keller ◽  
David J. Weir

2010 ◽  
Vol 19 (01) ◽  
pp. 58-63 ◽  
Author(s):  
C. G. Chute

Summary Objective: Can social computing efforts materially alter the distributed creation and maintenance of complex biomedical terminologies and ontologies; a review of distributed authoring history and status. Background: Social computing projects, such as Wikipedia, have dramatically altered the perception and reality of large-scale content projects and the labor required to create and maintain them. Health terminologies have become large, complex, interdependent content artifacts of increasing importance to biomedical research and the communities understanding of biology, medicine, and optimal healthcare practices. The question naturally arises as to whether social computing models and distributed authoring platforms can be applied to the voluntary, distributed authoring of high-quality terminologies and ontologies. Methods: An historical review of distributed authoring developments. Results: The trajectory of description logic-driven authoring tools, group process, and web-based platforms suggests that public distributed authoring is likely feasible and practical; however, no compelling example on the order of Wikipedia is yet extant. Nevertheless, several projects, including the Gene Ontology and the new revision of the International Classification of Disease (ICD-11) hold promise.


2019 ◽  
Vol 15 ◽  
pp. 100186
Author(s):  
Stefan Schulz ◽  
Philipp Daumke ◽  
Martin Romacker ◽  
Pablo López-García

2013 ◽  
Vol 103 ◽  
pp. 210-221 ◽  
Author(s):  
Feng Zheng ◽  
Zhan Song ◽  
Ling Shao ◽  
Ronald Chung ◽  
Kui Jia ◽  
...  

2010 ◽  
Vol 16 (4) ◽  
pp. 417-437 ◽  
Author(s):  
TIM VAN DE CRUYS

AbstractThe distributional similarity methods have proven to be a valuable tool for the induction of semantic similarity. Until now, most algorithms use two-way co-occurrence data to compute the meaning of words. Co-occurrence frequencies, however, need not be pairwise. One can easily imagine situations where it is desirable to investigate co-occurrence frequencies of three modes and beyond. This paper will investigate tensor factorization methods to build a model of three-way co-occurrences. The approach is applied to the problem of selectional preference induction, and automatically evaluated in a pseudo-disambiguation task. The results show that tensor factorization, and non-negative tensor factorization in particular, is a promising tool for Natural Language Processing (nlp).


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