Learning the Past Tense of English Verbs Using Inductive Logic Programming

. This paper presents results on using a new inductive logic programming method called Foidl to learn the past tense of English verbs. The past tense task has been widely studied in the context of the symbolic/connectionist debate. Previous papers have presented results using various neural-network and decision-tree learning methods. We have developed a technique for learning a special type of Prolog program called a first-order decision list, defined as an ordered list of clauses each ending in a cut. Foidl is based on Foil [19] but employs intensional background knowledge and avoids the need for explicit negative examples. It is particularly useful for problems that involve rules with specific exceptions, such as the past-tense task. We present results showing that Foidl learns a more accurate past-tense generator from significantly fewer examples than all previous methods. 1 Introduction The problem of learning the past tense of English verbs has been widely studied as an interesti..

Relational Learning Techniques for Natural Language Information Extraction

The recent growth of online information available in the form of natural language documents creates a greater need for computing systems with the ability to process those documents to simplify access to the information. One type of processing appropriate for many tasks is information extraction, a type of text skimming that retrieves specific types of information from text. Although information extraction systems have existed for two decades, these systems have generally been built by hand and contain domain specific information, making them difficult to port to other domains. A few researchers have begun to apply machine learning to information extraction tasks, but most of this work has involved applying learning to pieces of a much larger system. This paper presents a novel rule representation specific to natural language and a learning system, Rapier, which learns information extraction rules. Rapier takes pairs of documents and filled templates indicating the information to be ext..

Efficient and Effective Induction of First Order Decision Lists

Abstract. We present BUFOIDL, a new bottom-up algorithm for learning first order decision lists. Although first order decision lists have potential as a representation for learning concepts that include exceptions, such as language constructs, previous systems suffered from limitations that we seek to overcome in BUFOIDL. We present experiments comparing BUFOIDL to previous work in the area, demonstrating the system’s potential.

Relational learning of pattern-match rules for information extraction

Information extraction is a form of shallow text processing that locates a specified set of relevant items in a natural-language document. Systems for this task require significant domain-specific knowledge and are time-consuming and difficult to build by hand, making them a good application for machine learning. We present a system, Rapier, that uses pairs of sample documents and filled templates to induce pattern-match rules that directly extract fillers for the slots in the template

Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming

This paper demonstrates the capabilities of Foidl, an inductive logic programming (ILP) system whose distinguishing characteristics are the ability to produce first-order decision lists, the use of an output completeness assumption as a substitute for negative examples, and the use of intensional background knowledge. The development of Foidl was originally motivated by the problem of learning to generate the past tense of English verbs; however, this paper demonstrates its superior performance on two different sets of benchmark ILP problems. Tests on the finite element mesh design problem show that Foidl's decision lists enable it to produce generally more accurate results than a range of methods previously applied to this problem. Tests with a selection of list-processing problems from Bratko's introductory Prolog text demonstrate that the combination of implicit negatives and intensionality allow Foidl to learn correct programs from far fewer examples than Foil

Applying ILP-based Techniques to Natural Language Information Extraction: An Experiment in Relational Learning

Introduction In complex and context-rich domains, inductive logic programming (ILP) has some advantages over propositional, or feature-based, machine learning algorithms. The feature-based systems require that the examples be reduced to a finite, manageable set of features. Development of such a set of features can require significant representation engineering and may still exclude important contextual information. A first order logic representation can represent a richer set of features and more easily capture contextual information. ILP also allows the use of background knowledge, and the resulting rules are often more comprehensible. The comprehensibility of symbolic rules makes it easier for the system developer to understand and verify the resulting system and perhaps even edit the learned knowledge [ Cohen, 1996 ] . One domain with the complexity to make relational learning preferable to feature-based learning is natural language processing (NLP). Detailed experimenta

Learning the Past Tense of English Verbs Using . . .

This paper presents results on using a new inductive logic programming method called Foidl to learn the past tense of English verbs. The past tense task has been widely studied in the context of the symbolic/connectionist debate. Previous papers have presented results using various neural-network and decision-tree learning methods