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addition, we use the keyword extracting method, which is based on the maximum entropy model, to get rid of the useless information. The experimental results show that the keyword extracting algorithm can get 70% precision, and the condition probabilistic based algorithm is more precise than the token-based algorithm. HIMA
keyword specified by the investigator or suggested by system. Experiments were conducted on dummy crime dataset to test the accuracy and the scalability of the proposed system. Experimental results proved that subject suggestion improved the accuracy and thus speeds up the process of searching the evidence.
extract the keywords in each document. The paper establishes the transformation between the keywords in documents and the binary granules, and adopts the algorithm of association rules based on granular computing to obtain frequent item sets between documents. Bring in the set theory thought, numbers of the same word between
three different unsupervised learning methods for identifying topics among domain name keywords: spherical k-means clustering (SKM), Latent Dirichlet Allocation (LDA), and the Biterm Topic Model (BTM). We successfully infer semantically coherent groups of words in two independent data sets, finding that BTM topics are
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