This paper describes WordNet design and development, discussing its origins, the objectives it initially intended to reach and the subsequent use to which it has been put, the factor that has determined its structure and success. The emphasis in this description of the product is on its main applications, given the instrumental nature of WordNet, and on the improvements and upgrades of the tool itself, along with its use in natural language processing systems. The purpose of the paper is to identify the most significant recent trends with respect to this product, to provide a full and useful overview of WordNet for researchers working in the field of information retrieval. The existing literature is reviewed and present applications are classified to concur with the areas discussed at the First International WordNet Congress.

Web Information Extraction (WIE) systems have recently been able to extract massive quantities of relational data from online text. This has opened the possibility of achieving an elusive goal in Artificial Intelligence (AI): broad-coverage domain knowledge. AI systems depend to a great extent on having knowledge about the domains in which they operate, and such knowledge is typically expensive to enter into the system. Furthermore, the knowledge must be entered for every different domain in which an application is to operate. The Web contains knowledge about all kinds of different domains, but in a format that is not readily usable by AI systems. WIE promises to bridge the gap between the Web and AI. Natural Language Processing is an example of an area in AI in which knowledge can make a dramatic difference in the performance of an application. Understanding or interpreting language depends on the ability to understand the words used in a domain. The meanings, usages, and syntactic properties of words, and the relative frequency with which certain words are used, are necessary pieces of information for effective language processing, and much of this information can be extracted from text. In one case study, this thesis examines methods for using extracted information in improving a particular kind of language processing tool, a parser. Before information extraction can become broadly useful, however, more research must be done to improve the quality of the extracted information. A number of factors affect the quality, including correctness, importance or relevance, and the sophistication of meaning representation. The second case study in this thesis investigates a method for resolving synonyms in extracted information. This technique changes the meaning representation of extractions from one that relates words or names to one that relates entities to one another.

Most of the information stored in digital form is hidden in natural language texts. Extracting and storing it in a formal representation (e.g. in form of relations in databases) allows efficient querying, easy administration and further automatic processing of the extracted data. The area of information extraction (IE) comprises techniques, algorithms and methods performing two important tasks: finding (identifying) the desired, relevant data and storing it in appropriate form for future use. The rapidly increasing number and diversity of IE systems are the evidence of continuous activity and growing attention to this field. At the same time it is becoming more and more difficult to overview the scope of IE, to see advantages of certain approaches and differences to others. In this paper we identify and describe promising approaches to IE. Our focus is adaptive systems that can be customized for new domains through training or the use of external knowledge sources. Based on the observed origins and requirements of the examined IE techniques a classification of different types of adaptive IE systems is established.

• A hybrid approach for extracting semantic relations from texts. 2nd Workshop on Ontology

this article was the president of the Massachusetts Golf Association only because CAMP recognized that the "he" in the second sentence is coreferent with "John Perry" in the first. And it is this fact which actually helps VSM-Disambiguate decide that the two John Perrys in doc.36 and doc.38 are the same person

Information Extraction (IE) systems often use patterns to identify relevant information in text but these are difficult and time-consuming to generate manually. This paper presents a new approach to the automatic learning of IE patterns which uses WordNet to judge the similarity between patterns. The algorithm starts with a small set of sample extraction patterns and uses a similarity metric, based on a version of the vector space model augmented with information from WordNet, to learn similar patterns. This approach is found to perform better than a previously reported method which relied on information about the distribution of patterns in a corpus and did not make use of WordNet.

Many applications in natural language processing benefit from the use of word senses rather than surface word forms. While the use of word senses has historically required large, manually compiled dictionaries, recent work has focused on automatically inducing these senses from unannotated text. This paper presents an overview of the task of unsupervised word sense induction (WSI) and compares several approaches to the task, concluding with a final overview of the techniques surveyed. 1