CA-LDA

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• The model is implemented on top of and requires Mallet 2.0.7's ParallelTopicModel.
• Trained models are available for download:
  • 200 dimensional
  • 500 dimensional

Input/Output

• Given a document from one of the context it has been trained on (e.g. Mail, Meetup, Twitter, and News), CA-LDA returns the K-dimensional topic distribution of the document along with the proportion of the background words in the document.
• The returned K-dimensional topic distribution can be used to estimate the similarity between two documents from different context with the influence of the context-depandent background words removed.
• Cosine similairty is recommended similarity metric.

Usage

Preprocessing

• Create a preprocessing Pipe.
  • The pipe structure needs to be exactly the same as the one we used when training the model (See example.)
• Put raw documents into an InstanceList throuhg the pipe (See example.)

Inference

• Load the model using ObjectStreamInput.readObject()
• Call model.getInferencer(contextName) to get a TopicInferencer for a specific context.
• Infer the topic distribution by calling inferencer.getSampledDistribution(instance, 100, 1, 5).
• The function returns a dobule[] of length K+1 which consists of the distribution of each K topic plus the proportion of the background words (at the last element of the array).
  • Usually, we discard the background proportion and only use the K-dimentional topic distribution to estimate the document similarity

Demo

• For details, please see Demo App

  java -classpath [CLASSES_LOCATION] cc.mallet.examples.RunContextAwareLDA [MODEL_FILE] [DOCUMENT] [CONTEXT_NAME]

  • Sample result from inferring topics in one of the EnronSent document (using Mail context)

  > java -classpath CALDA.jar cc.mallet.examples.RunContextAwareLDA CA_LDA_dim_500.bin.gz enronsent04 Mail
  
  Loading model ...
  
  Found 4 contexts: Mail, Meetup, News, Twitter
  
  Topic distribution:
  0.044 of Topic 217: energi power solar electr gas renew fuel wind util effici
  0.029 of Topic 159: messag send account list inform receiv contact pleas updat issu
  0.025 of Topic 424: invest stock market investor compani fund financi firm report ceo
  0.023 of Topic 279: law legal court rule lawyer protect licens patent copyright contract
  0.023 of Topic 395: program fund propos feder provid public budget administr nation bill
  ....