Using Alignment-Based Learning for Unsupervised Grammar Learning

Title: Using Alignment-Based Learning for Unsupervised Grammar Learning

Abstract: This research introduces a new type of unsupervised learning algorithm called Alignment-Based Learning (ABL). The algorithm uses the alignment of sentences to find possible constituents in the form of labelled brackets. The main goal of the algorithm is to automatically find constituents in plain sentences without using additional information. The algorithm was applied to two corpora: ATIS and OVIS. The results show that even the simplest ABL algorithm based on alignment can learn recursion. This paper discusses the algorithm's implications, comparisons with other grammar learning algorithms, and future research.

Research Question: Can an unsupervised learning algorithm based on sentence alignment learn grammar structure from plain sentences without using additional information?

Methodology: The ABL algorithm consists of two phases: alignment learning and selection learning. In the alignment learning phase, the algorithm aligns all sentences in pairs to find similar and dissimilar parts. These parts are then grouped and labelled with a non-terminal, indicating possible constituents. In the selection learning phase, the algorithm selects non-overlapping constituents.

Results: The ABL algorithm was applied to two corpora: ATIS and OVIS. The results showed that the algorithm could learn recursion and improve the structure of the sentences.

Implications: The ABL algorithm's ability to learn recursion without using additional information suggests that it could potentially revolutionize the field of unsupervised grammar learning. This could lead to the development of new algorithms and applications in natural language processing and machine learning.

Comparison with Other Algorithms: ABL is different from other grammar learning algorithms because it uses sentence alignment to find constituents, rather than relying on specific features or rules. This makes it more adaptable and easier to apply to different types of sentences.

Future Research: Future research will focus on improving the ABL algorithm's performance by incorporating more sophisticated alignment techniques and developing methods to handle ambiguous sentences. Additionally, researchers will explore applying the ABL algorithm to other natural language processing tasks, such as machine translation and text summarization.

Link to Article: https://arxiv.org/abs/0104007v1 Authors: arXiv ID: 0104007v1