Passage re-ranking is a machine learning task that estimates relevance scores between given query and candidate passages. Keyword features based on the lexical similarities queries passages have been traditionally used for passage models. However, such approaches limitation; it difficult to find semantic contextual beyond word-matching information. Recently, several studies neural pre-trained l...

Keyword-based query model has been an immediate and eecient way to specify and retrieve related information on the Web. However, conventional document ranking based on an automatic assessment of document relevance to the query may not be the best approach when little information is given as in most cases. In order to clarify the ambiguity of the short queries given by users, we propose concept-...

We propose a general relevance model, called the User Relevance Model, that formalises the decisions taken by a user during a query with respect to relevance judgements. Starting from a keyword-based query, the user is allowed to refine the document search using relevance feedback iterations where some subset of the result set is marked as relevant, and another subset is marked as non-relevant....

Page Managing Software Development Risk with the OODA Loop Nathaniel D. Amsden, Narasimha K. Shashidhar Doi : 10.7321/jscse.v2.n7.1 Abstract . Software development projects are subject to risks like any other project. These risks must be managed in order for the project to succeed. Current frameworks and models for risk identification, assessment and management are static and unchanging. They l...

Emergent Semantics is a new paradigm for inferring semantic meaning from implicit feedback by a sufficiently large number of users of an object retrieval system. In this paper, we introduce a universal architecture for emergent semantics using a central repository within a multi-user environment, based on solid linguistic theories. Based on this architecture, we have implemented an information ...