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Language Modeling (LM) has been successfully applied to Information Retrieval (IR). However, most of the existing LM approaches only rely on term occurrences in documents, queries and document collections. In traditional unigram based models, terms (or words) are usually considered to be independent. In some recent studies, dependence models have been(More)
Domain specific information retrieval has become in demand. Not only domain experts, but also average non-expert users are interested in searching domain specific (e.g., medical and health) information from online resources. However, a typical problem to average users is that the search results are always a mixture of documents with different levels of(More)
With the increasing popularity of e-marketplaces for B2C and B2B e-commerce, intelligent software agents are promising in improving the effectiveness of these markets by autonomously searching for products and negotiating contracts on behalf of the human decision makers. A large number of research has been conducted to develop negotiation protocols and(More)
The language modelling approach to information retrieval can also be used to compute query models. A query model can be envisaged as an expansion of an initial query. The more prominent query models in the literature have a probabilistic basis. This paper introduces an alternative, non-probabilistic approach to query modelling whereby the strength of(More)
Experimental approaches are widely employed to benchmark the performance of an information retrieval (IR) system. Measurements in terms of recall and precision are computed as performance indicators. Although they are good at assessing the retrieval effectiveness of an IR system, they fail to explore deeper aspects such as its underlying functionality and(More)
Humans can make hasty, but generally robust judgements about what a text fragment is, or is not, about. Such judgements are termed information inference. By drawing on theories from non-classical logic and applied cognition, an information inference mechanism is proposed which makes inferences via computations of information flow through a high dimensional(More)
Information retrieval (IR) is driven by a process which decides whether a document is about a query. Recent attempts spawned from logic-based information retrieval theory have formalized properties characterizing “aboutness”, but no consensus has yet been reached. The proposed properties are largely determined by the underlying framework within which(More)
With the wide spread applications of e-Learning technologies to education at all levels, increasing number of online educational resources and messages are generated from the corresponding e-Learning environments. Accordingly, instructors are often overwhelmed by the huge number of messages created by students through online discussion forums. It is quite(More)
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The Relevance-based language model is a promising invention within the language modeling approach to document retrieval [4]. The relevance model computes ) | Pr( R w which is interpreted as the “probability of observing a word w in documents relevant to an information need”. In practice, this probability is approximated by ) ,..., , | Pr( 2 1 k q q q w for(More)