Description: This paper introduces the semantic wildcard. The IRSLO (Information Retrieval using Semantic and Lexical Operators) project aims at integrating semantic and lexical information into the retrieval process, in order to overcome some of the impediments currently encountered with today's information retrieval systems. This paper introduces the semantic wildcard, one of the most powerful operators implemented in IRSLO, which allows for searches along general-specific lines. The semantic wildcard, denoted with #, acts in a manner similar with the lexical wildcard, but at semantic levels, enabling the retrieval of subsumed concepts. For instance, a search for animal# will match any concept that is of type animal, including dog, goat, and so forth, thereby going beyond the explicit knowledge stated in texts. This operator, together with a lexical locality operator that enables the retrieval of paragraphs rather than entire documents, have been both implemented in the IRSLO system and tested on requests of information run against an index of 130,000 documents. Significant improvement was observed over classic keyword-based retrieval systems in terms of precision, recall and success rate.

Description: This paper discusses dynamic agent population in agent-based distance vector routing. Abstract: The Intelligent mobile agent paradigm can be applied to a wide variety of intrinsically parallel and distributed applications. Network routing is one such application that can be mapped to an agent-based approach. The performance of any agent-based system will depend on its agent population. Although a lot of research has been conducted on agent-based systems, little consideration has been given to the importance of agent population in dynamic networks. A large number of constituent agents can increase the resource overhead of the system, thereby impeding the overall performance of the network. Hence, it is imperative to find the optimal number of agents in the system that would maximize the efficiency of the agent-based mechanism in the network. This optimal value cannot be determined manually, thereby emphasizing the need for an adaptive approach that manipulates the number of agents in the system based on its resource availability. This paper discusses an agent-based approach to Distance Vector Routing, referred as Agent-based Distance Vector Routing and also describes an adaptive approach controlling the number of agents in the network using pheromones and discusses their limitations.

Description: This paper discusses letter level learning for language independent diacritics restoration. Abstract: This paper presents a method for diacritics restoration based on learning mechanisms that act at letter level. The method requires no additional tagging tools or resources other than raw text, which makes it independent of the language, and particularly appealing for languages for which there are few resources available. The algorithm was evaluated on four different languages, namely Czech, Hungarian, Polish, and Romanian, and an average accuracy of over 98% was observed.

Description: This paper discusses metadata utilization. Abstract: Metadata schemes emerge to meet community and user requirements, and they evolve over time to meet changing requirements. This paper reports results of an analysis of a large sample of MARC 21 bibliographic records. MARC 21 is an encoding scheme related closely to metadata elements occurring in library bibliographic records. The records were analyzed for the utilization of content designation available in MARC 21. Results indicate that less than 5% of available content designation accounts for over 80% of occurrences. The implications of these findings affect indexing policies, system design, and can inform setting requirements for extending a metadata scheme based on a threshold of community requirements.

Description: In this paper, the author attempts to make a formal analysis of the performance in automatic part of speech tagging. Lower and upper bounds in tagging precision using existing taggers or their combination are provided. Since the author shows that with existing taggers, automatic perfect tagging is not possible, two solutions for applications requiring very high precision are presented: (1) a solution involving minimum human intervention for a precision of over 98.7%, and (2) a combination of taggers using a memory based learning algorithm that succeeds in reducing the error rate with 11.6% with respect to the best tagger involved.