Learning Rules and Their Exceptions
We present in this article a top-down inductive system, ALLiS, for learning linguistic structures.
Two difficulties came up during the development of the system: the presence of a significant amount of noise in the data and the presence of exceptions linguistically motivated.
It is then a challenge for an inductive system to learn rules from this kind of data.
This leads us to add a specific mechanism, refinement
, which enables learning rules and their exceptions.
In the first part of this article we evaluate the usefulness of this device and show that it improves results when learning linguistic structures.
In the second part, we explore how to improve the efficiency of the system by using prior knowledge.
Since Natural Language is a strongly structured object, it may be important to investigate whether linguistic knowledge can help to make natural language learning more efficiently and accurately.
This article presents some experiments demonstrating that linguistic knowledge improves learning.
The system has been applied to the shared task of the CoNLL'00 workshop.