stephenhky/PyShortTextCategorization

What it solves

It addresses the difficulty of categorizing short texts, which often lack enough information and suffer from word sparseness. The package provides tools to create intermediate representations of texts before they are processed by classification algorithms.

How it works

The library implements various text representation techniques, including topic modeling (LDA, LSI, Random Projections) and word-embedding algorithms. It then applies these representations to supervised or unsupervised learning tasks using a variety of classifiers, such as neural networks (ConvNet, C-LSTM), maximum entropy, and cosine distance classification.

Who it’s for

Developers and data scientists working with short-form text data who need a comprehensive toolkit for preprocessing, representation, and classification.

Highlights

• Support for pre-trained word embeddings and gensim topic models.
• Integration with scikit-learn for supervised learning.
• Advanced classification options including ConvNet and C-LSTM neural networks.
• Text utility tools such as spell correction and character-level seq2seq learning.
• Phrase difference metrics like Word Mover's distance (WMD) and soft Jaccard score.