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Because the entities extracted using the method discussed in this paper have very high precision and are run against a decent size of document set, they will make a very good training set for ML algorithms.
In this study, we design and experiment a parallel k-means algorithm using MapReduce programming model and compared the result with sequential k-means for clustering varying size of document dataset.
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Table 7 Statistics of the document size distribution Average size of documents 288 Median size of documents 90 Size of largest document 126,712 Size of smallest document 1.
The average size of documents is higher than the median which means this distribution is skewed.
Further, there is a large variability in the size of documents in the collection.
The data set is divided into an increasing size of documents simulating interactive annotation.
With the popularity of Internet and World Wide Web (WWW, Web), the size of documents on the Web grows dramatically.
In our method, we determined the precision by utilizing a large representative sample (determined statistically) of extracted entities which were drawn from a large size of documents.
Initially, we decided to investigate the size of documents, that is the full text without HTML tags, in order to choose good sizes for the passages to be used in our experiments.
To agree on scoring, researchers discussed differences in judgement on the appropriate size of documents, the usefulness of diagrams and how to define the criteria for scoring referenced statements and contact details for help.
Obviously, the generalizability and conclusiveness of the results is limited by the size of the document collection and the analysed documents.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com