Semantic Text Segmentation with Embeddings

Given a text document d and a desired number of segments k, this repo shows how to segment the document into k semantically homoginuous segments.

The general approach is as follows:

1. Convert the words in d into embedding vectors using the GloVe model.
2. For all word sequences s in d: the average meaning (centroid) of s is represented by taking the average embeddings of all words in s.
3. The error in the centroid calculation for a sequence s is calculated as the average cosine distance between the centroid and all words in s.
4. The segmentation is done using the greedy heuristic by iteratively choosing the best split point p.

The class text_segmentation_class in text_segmetnation.py contains funtions to convert the document words in GloVe embeddings and choose the splitting points. The notebook semantc_text_segmentation_example.ipynb demonstrates how to use the class.

Technologies

This project was developed using:

• python=3.5
• gensim==3.7.1
• matplotlib==2.2.3
• mpld3==0.3
• nltk==3.4
• numpy==1.14.5
• pandas==0.23.4
• parso==0.3.1
• Pillow==5.2.0
• scikit-learn==0.19.2
• scipy==1.1.0