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基于中文的Semi-supervised Recursive Autoencoders


Twitter data from the first 2008 Presidential debate Total number of tweets is 3,238

Data is in ./data folder:

  • rawdata.tsv - the whole data set
  • test.tsv - a small set for testing purpose
  • trainset - the training data
  • testset - the test data

Prerequisite

The system is buit on Python 2.7

Other packages you need to install before running the system:

  • numpy
  • scikit learn
  • matplotlib
  • nltk

How to use

  1. Download the code from github

  2. run the following depending on the usage

    • for cross validation of the training data, run:

    crossvalidation.py data/model.json

    • for analysis, run:

    analysis.py data/model.json

    • for training and predict test data, run:

    main.py data/model.json

Model configuration

Parameters of the model can be changed in ./data/model/json

  • d : dimension of the word vector
  • cat : number of categories of the classification problem
  • alpha : the proportion of supervised (classification) error and unsupervised (reconstruction) error
  • lambdaW : regularisation term on word vector reconstruction matrices
  • lambdaCat : regularisation term on category
  • lambdaL : regularisation term on word embedding
  • iter : number of maximum iteration of the minFunc solver =======

rnn

4d60b68eac2a8f8ab6e0d14db8219984e7ede1f5

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