Building expertise profiles in global online communities is a critical step in leveraging the range of expertise available in the global knowledge economy. In this paper we introduce a three-stage framework that automatically generates expertise profiles of online community members. In the first two stages, document-topic relevance and user-document association are estimated for calculating use...

Research on cross-language information retrieval (CLIR) has typically been restricted to settings using binary relevance assessments. In this paper, we present evaluation results for dictionary-based CLIR using graded relevance assessments in a best match retrieval environment. A text database containing newspaper articles and a related set of 35 search topics were used in the tests. First, mon...

1. Introduction This paper addresses three questions about the Language Modelling (LM) approach to information retrieval. These questions are about LM and relevance. They arise because relevance has always been taken as fundamental to information retrieval (see, e.g. Saracevic, 1975, or Mizzaro, 1997). Thus from the standpoint of retrieval theory, the presumption has been that as relevance is t...

This article provides a comprehensive and comparative overview of question answering technology. It presents the question answering task from an information retrieval perspective and emphasises the importance of retrieval models, i.e., representations of queries and information documents, and retrieval functions which are used for estimating the relevance between a query and an answer candidate...

The coordination of time-dependent simulation models is an important problem in environmental systems engineering. We propose a design model based on a formal specification using the CSP-like language FSP of Magee and Kramer. The heart of our design model is a global timecontroller which coordinates distributed simulation models according to their local time scales. We show how a systematic tra...

Natural Language Processing (NLP) has become one of the leading application areas in current Artificial Intelligence boom. Transfer learning enabled large deep neural networks trained on language modeling task to vastly improve performance almost all language tasks. Interestingly, when models are with data that includes software code, they demonstrate remarkable abilities gener...

The paper presents an ontology-based approach to monitor and assess changes and trends of technology intensive domains. The research, produced in collaboration with a variety of chemical engineering users of knowledge, promotes ontologies and semantically enriched models to enable knowledge sharing, classification, monitoring and reuse. Ontologies are deployed to search, retrieve and analyze te...

We approached the problem as learning how to order documents by estimated relevance with respect to a user query. Our support vector machines based classifier learns from the relevance judgments available with the standard test collections and generalizes to new, previously unseen queries. For this, we have designed a representation scheme, which is based on the discrete representation of the l...