A Hierarchical Clustering Based Feature Word Extraction Method

Author(s):  
Yihao Li ◽  
Zheng Hong ◽  
Wenbo Feng ◽  
Lifa Wu
2019 ◽  
Vol 32 (3) ◽  
pp. 223-238 ◽  
Author(s):  
Jiang Qian ◽  
Ruixin Zhao ◽  
Jingkang Wei ◽  
Xiaohui Luo ◽  
Yilan Xue

Author(s):  
Ruipeng Yang ◽  
Dan Qu ◽  
Yekui Qian ◽  
Yusheng Dai ◽  
Shaowei Zhu

2013 ◽  
Vol 427-429 ◽  
pp. 2489-2492 ◽  
Author(s):  
Tian Yu Zhao ◽  
Jian Yi Liu ◽  
Ru Zhang

Rich information is contributed to microblogs by millions of users all around the world. However, few work has been done on the study of microblog web page extraction so far. We proposed a unified structured information extraction method based on hierarchical clustering which is suitable for microblog web pages of any microblog websites. The experiment result on microblog web pages of some popular microblog service providers indicates the high performance of our method.


Author(s):  
Douglas C. Barker

A number of satisfactory methods are available for the electron microscopy of nicleic acids. These methods concentrated on fragments of nuclear, viral and mitochondrial DNA less than 50 megadaltons, on denaturation and heteroduplex mapping (Davies et al 1971) or on the interaction between proteins and DNA (Brack and Delain 1975). Less attention has been paid to the experimental criteria necessary for spreading and visualisation by dark field electron microscopy of large intact issociations of DNA. This communication will report on those criteria in relation to the ultrastructure of the (approx. 1 x 10-14g) DNA component of the kinetoplast from Trypanosomes. An extraction method has been developed to eliminate native endonucleases and nuclear contamination and to isolate the kinetoplast DNA (KDNA) as a compact network of high molecular weight. In collaboration with Dr. Ch. Brack (Basel [nstitute of Immunology), we studied the conditions necessary to prepare this KDNA Tor dark field electron microscopy using the microdrop spreading technique.


Planta Medica ◽  
2008 ◽  
Vol 74 (09) ◽  
Author(s):  
JR Tormo ◽  
N Tabanera ◽  
D Conway ◽  
P Ramos ◽  
A Redondo ◽  
...  

Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%


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