A MACHINE LEARNING APPROACH TO KNOWLEDGE ACQUISITIONS FROM TEXT DATABASES

The rapid growth of data in large databases, such as text databases an d scientific databases, requires efficient computer methods for automa ting analyses of the data with the goal of acquiring knowledges or mak ing discoveries. Because the analyses of data are generally so expensi ve, most parts in databases remains as raw, unanalyzed primary data. T echnology from machine learning (ML) will offer efficient tools for th e intelligent analyses of the data using generalization ability. Gener alization is an important ability specific to inductive learning that will predict unseen data with high accuracy based on learned concepts from training examples. In this article, we apply ML to text-database analyses and knowledge acquisitions from text databases. We propose a completely new approach to the problem of text classification and extr acting keywords by using ML techniques. We introduce a class of repres entations for classifying text data based on decision trees; (i.e., de cision trees over attributes on strings) and present an algorithm for learning them inductively. Our algorithm has the following features: I t does not need any natural language processing technique and it is ro bust for noisy data. We show that our learning algorithm can be used f or automatic extraction of keywords for text retrieval and automatic t ext categorization. We also demonstrate some experimental results usin g our algorithm on the problem of classifying bibliographic data and e xtracting keywords in order to show the effectiveness of our approach.