Acquiring information extraction patterns from unannotated corpora

Information Extraction (IE) can be defined as the task of automatically extracting preespecified kind of information from a text document. The extracted information is encoded in the required format and then can be used, for example, for text summarization or as accurate index to retrieve new documents.The main issue when building IE systems is how to obtain the knowledge needed to identify relevant information in a document. Today, IE systems are commonly based on extraction rules or IE patterns to represent the kind of information to be extracted. Most approaches to IE pattern acquisition require expert human intervention in many steps of the acquisition process. This dissertation presents a novel method for acquiring IE patterns, Essence, that significantly reduces the need for human intervention. The method is based on ELA, a specifically designed learning algorithm for acquiring IE patterns from unannotated corpora.The distinctive features of Essence and ELA are that 1) they permit the automatic acquisition of IE patterns from unrestricted and untagged text representative of the domain, due to 2) their ability to identify regularities around semantically relevant concept-words for the IE task by 3) using non-domain-specific lexical knowledge tools such as WordNet and 4) restricting the human intervention to defining the task, and validating and typifying the set of IE patterns obtained.Since Essence does not require a corpus annotated with the type of information to be extracted and it does makes use of a general purpose ontology and widely applied syntactic tools, it reduces the expert effort required to build an IE system and therefore also reduces the effort of porting the method to any domain.In order to Essence be validated we conducted a set of experiments to test the performance of the method. We used Essence to generate IE patterns for a MUC-like task. Nevertheless, the evaluation procedure for MUC competitions does not provide a sound evaluation of IE systems, especially of learning systems. For this reason, we conducted an exhaustive set of experiments to further test the abilities of Essence.The results of these experiments indicate that the proposed method is able to learn effective IE patterns

A Description Logic System for Learning in Complex Domains

This paper introduces YAYA 1 , a Description Logic system focused towards learning complex interrelations among objects. YAYA Concept Language (YCL) is quite restricted; although it introduces a limited use of variables. Language limited expressiveness in combination with a special inference mechanism allow YAYA to achieve its main goal: to acquire a model (TBox) from a set of examples that allows it to complete the information of new sets of (incomplete) examples from the same domain. 1 Introduction Knowledge structuring and reasoning services provided by Description Logics can be very useful for Machine Learning tasks. Nevertheless, little attention has been paid to defining Concept Languages suitable for acquiring the model we will use for reasoning purposes. CCLASSIC [ Ventos, 1996 ] is an example. [ Cohen and Hirsh, 1994 ] study learnability properties of CoreClassic, proposing an efficiently learnable subset of it. In this paper we introduce a new Description Logic system cal..