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qf6101
Implementation of paper: Deng K, Bol P K, Li K J, et al. On the unsupervised analysis of domain-specific Chinese texts[J]. Proceedings of the National Academy of Sciences, 2016: 201516510.
TopWORDS
Brief Description
This project is an implementation of TopWORDS algorithm proposed in the following paper.
Deng K, Bol P K, Li K J, et al. On the unsupervised analysis of domain-specific Chinese texts[J]. Proceedings of the National Academy of Sciences, 2016: 201516510.
TopWORDS can achieve word discovery and text segmentation simultaneously for Chinese texts. It is designed to be fast and use very little memory. In my experiment, it takes around 5 minutes to segment "The Story of Stone" with an Intel i3-4160 CPU and less than 2G memory. This implementation is based on Spark 2.0.0 which means it can be used in either a local machine with predefined number of threads or yarn cluster for large amount of texts.
For more information about its theory, refer to http://qf6101.github.io/machine%20learning/2016/07/01/TopWORDS (in Chinese)
Local Machine Mode
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Download Spark 2.0.0 from http://spark.apache.org/downloads.html
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Set the parameters in deploy/sbin/topwords_local.sh (simply set only SPARK_HOME if you just need to run "The Story of Stone" example)
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Run the script: bash deploy/sbin/topwords_local.sh
Yarn Cluster Mode
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Set the parameters in deploy/sbin/topwords_yarn.sh
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Run th script: bash deploy/sbin/topwords_yarn.sh (you may need to initialize the keytab in advance)
And for reference, in my experiment it takes around 40 minutes to segment nearly 7 million sentences using the default parameters in script.
API Usage
Please refer to src/test/scala/io/github/qf6101/topwords/TestTopWORDS.scala
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