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.