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Symbolic grammatical inference framework (for unsupervised machine learning)

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Alignment-Based Learning

Alignment-Based Learning (ABL) is a symbolic grammar inference framework that has succesfully been applied for several unsupervised machine learning tasks in Natural Language Processing (NLP).

Given sequences of symbols only, a system that implements ABL induces structure by aligning and comparing the input sequences. As a result, the input sequences are augmented with the induced structure. For more information, see the reference list at the end of this file.

This package contains a C++ implementation of ABL:

  • Developed by Menno van Zaanen and Jeroen Geertzen
  • Maintained by Menno, who distributes stable releases including documentation here
  • A brief explanation and an online demo that can be played with can be found here

This repository contains a development version and may differ from the latest stable release.

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Symbolic grammatical inference framework (for unsupervised machine learning)

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