textsplit Document Segmenter

A textsplit Document Segmenter is a document segmentation library.

• Example(s):
  • textsplit, v0.5 [1].
  • ...
• Counter-Example(s):
  • spaCy.
• See: Document Segmenter.

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References

2024

• https://pypi.org/project/textsplit/
  • NOTES:
    • **Purpose and Scope**: textsplit is a Python library designed to segment documents into coherent parts, especially useful for texts lacking clear paragraph annotations, such as those found in scraped PDFs or HTML documents.
    • **Methodology**: It utilizes word embeddings to determine the optimal segmentation points in a document, aiming for coherence within segments based on the accumulated weighted cosine similarity of words to their segment's mean vector.
    • **Coherence Interpretation**: The algorithm interprets segment coherence as related to the length of the segment vector in the vector space, assuming segments of similar lengths contain comparable amounts of information.
    • **Formalization**: The objective is to optimize the sum of the L2 norms of segment vectors formed by words between chosen split points, applying penalties for each split to prevent overly granular segmentation.
    • **Algorithms**: Offers two algorithms for segmentation - a fast, greedy approach and a more precise, dynamic programming method, both of which rely on a penalty hyperparameter to control segmentation granularity.
    • **Penalty Hyperparameter**: This crucial parameter influences the granularity of segmentation; the greedy algorithm can function without it by limiting segment numbers, while it helps fine-tune the dynamic programming approach.
    • **Accuracy Measurement**: The library includes methods to measure segmentation accuracy against reference segmentations, facilitating algorithm evaluation and adjustment.
    • **Usage and Input**: Algorithms process a matrix of text content vectors, ideally using word2vec embeddings, to identify optimal segmentation points, focusing on sentence vectors for paragraph segmentation.
    • **Practical Implementation**: A Jupyter notebook within the library offers guidance on using textsplit, including how to train word2vec vectors on a corpus and apply the module for text segmentation, suggesting improved results with larger corpora.

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