Short Text Mining using Advanced Keras Layers and Maxent: shorttext 0.4.1

On 07/28/2017, shorttext published its release 0.4.1, with a few important updates. To install it, type the following in the OS X / Linux command line:

>>> pip install -U shorttext

The documentation in PythonHosted.org has been abandoned. It has been migrated to readthedocs.org. (URL: http://shorttext.readthedocs.io/ or http:// shorttext.rtfd.io)

Exploiting the Word-Embedding Layer

This update is mainly due to an important update in gensim, motivated by earlier shorttext‘s effort in integrating scikit-learn and keras. And gensim also provides a keras layer, on the same footing as other neural networks, activation function, or dropout layers, for Word2Vec models. Because shorttext has been making use of keras layers for categorization, such advance in gensim in fact makes it a natural step to add an embedding layer of all neural networks provided in shorttext. How to do it? (See shorttext tutorial for “Deep Neural Networks with Word Embedding.”)

import shorttext
wvmodel = shorttext.utils.load_word2vec_model('/path/to/GoogleNews-vectors-negative300.bin.gz')   # load the pre-trained Word2Vec model
trainclassdict = shorttext.data.subjectkeywords()   # load an example data set

 

To train a model, you can do it the old way, or do it the new way with additional gensim function:

kmodel = shorttext.classifiers.frameworks.CNNWordEmbed(wvmodel=wvmodel, nb_labels=len(trainclassdict.keys()), vecsize=100, with_gensim=True)   # keras model, setting with_gensim=True
classifier = shorttext.classifiers.VarNNEmbeddedVecClassifier(wvmodel, with_gensim=True, vecsize=100)   # instantiate the classifier, setting with_gensim=True
classifier.train(trainclassdict, kmodel)

The parameters with_gensim in both CNNWordEmbed and VarNNEmbeddedVecClassifier are set to be False by default, because of backward compatibility. However, setting it to be True will enable it to use the new gensim Word2Vec layer.

These change in gensim and shorttext are the works mainly contributed by Chinmaya Pancholi, a very bright student at Indian Institute of Technology, Kharagpur, and a GSoC (Google Summer of Code) student in 2017. He revolutionized gensim by integrating scikit-learn and keras into gensim. He also used what he did in gensim to improve the pipelines of shorttext. He provided valuable technical suggestions. You can read his GSoC proposal, and his blog posts in RaRe Technologies, Inc. Chinmaya has been diligently mentored by Ivan Menshikh and Lev Konstantinovskiy of RaRe Technologies.

Maxent Classifier

Another important update is the adding of maximum entropy (maxent) classifier. (See the corresponding tutorial on “Maximum Entropy (MaxEnt) Classifier.”) I will devote a separate entry on the theory, but it is very easy to use it,

import shorttext
from shorttext.classifiers import MaxEntClassifier

classifier = MaxEntClassifier()

Use the NIHReports dataset as the example:

classdict = shorttext.data.nihreports()
classifier.train(classdict, nb_epochs=1000)

The classification is just like other classifiers provided by shorttext:

classifier.score('cancer immunology') # NCI tops the score
classifier.score('children health') # NIAID tops the score
classifier.score('Alzheimer disease and aging') # NIAID tops the score

Continue reading “Short Text Mining using Advanced Keras Layers and Maxent: shorttext 0.4.1”

Release of shorttext 0.3.3

On November 21, 2016, the Python package `shorttext’ was published. Until today, more than seven versions have been published. There have been a drastic architecture change, but the overall purpose is still the same, as summarized in the first introduction entry:

This package `shorttext‘ was designed to tackle all these problems… It contains the following features:

  • example data provided (including subject keywords and NIH RePORT);
  • text preprocessing;
  • pre-trained word-embedding support;
  • gensim topic models (LDA, LSI, Random Projections) and autoencoder;
  • topic model representation supported for supervised learning using scikit-learn;
  • cosine distance classification; and
  • neural network classification (including ConvNet, and C-LSTM).

And since the first version, there have been updates, as summarized in the documention (News):

Version 0.3.3 (Apr 19, 2017)

  • Deleted CNNEmbedVecClassifier.
  • Added script ShortTextWord2VecSimilarity.

Version 0.3.2 (Mar 28, 2017)

  • Bug fixed for gensim model I/O;
  • Console scripts update;
  • Neural networks up to Keras 2 standard (refer to this).

Version 0.3.1 (Mar 14, 2017)

  • Compact model I/O: all models are in single files;
  • Implementation of stacked generalization using logistic regression.

Version 0.2.1 (Feb 23, 2017)

  • Removal attempts of loading GloVe model, as it can be run using gensim script;
  • Confirmed compatibility of the package with tensorflow;
  • Use of spacy for tokenization, instead of nltk;
  • Use of stemming for Porter stemmer, instead of nltk;
  • Removal of nltk dependencies;
  • Simplifying the directory and module structures;
  • Module packages updated.

Although there are still additions that I would love to add, but it would not change the overall architecture. I may add some more supervised learning algorithms, but under the same network. The upcoming big additions will be generative models or seq2seq models, but I do not see them coming in the short term. I will add corpuses.

I may add tutorials if I have time.

I am thankful that there is probably some external collaboration with other Python packages. Some people have already made some useful contributions. It will be updated if more things are confirmed.

Continue reading “Release of shorttext 0.3.3”

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