This package provides a set of tools for working with topic modelling and in particular in the cross-lingual case, and for application to machine translation. The following algorithms are implemented

• Latent Dirichlet Allocation
• Cross-Lingual Explicit Semantic Analysis

And the following are planned

• Kernel Explicit Semantic Analysis
• Latent Semantic Analysis
• Coupled Probabilistic Latent Semantic Analysis

Translation Topics uses Maven to build, and can be simply installed with the following command

mvn install

To build a corpus for this there are existing scripts that download the data from Wikipedia. These can be run with (for English to German)

./build-wikipedia-article.sh en de

Mate-finding trials can be run with the following command, from the experiments sub-folder:

mvn exec:java -Dexec.mainClass=eu.monnetproject.bliss.experiments.MateFindingTrial 
       -Dexec.args="trainFile metricFactory W testFile"

Where W is the number of distinct tokens in the corpus and metricFactory is:

• eu.monnetproject.bliss.clesa.CLESA: For CL-ESA
• (More to come)

Language models can be trained with the following command (from the betalm folder)

mvn exec:java -Dexec.mainClass="betalm.compile" -Dexec.args="corpus.gz N wordMap W lmFile"

Where N is the order of the n-gram model and W the number of distinct tokens. To adapt to a specific document provide in addition to -Dexec.args the following flags

    -Dexec.args="-b METHOD -f file[.gz] ..." 

Where METHOD is one of

• COS_SIM
• NORMAL_COS_SIM
• KLD
• JACCARD
• DICE
• ROGERS_TANIMOTO
• DF_JACCARD
• DF_DICE
• WxWCLESA