Information retrieval is the task of ranking a list of documents or search results in response to a query
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We study how different levels of detail in knowledge representation influence the capability of guiding the user in the exploration of a complex information space.
We argue that the explicit management of ontological knowledge, and of the meaning of concepts (by integrating linguistic and encyclopedic knowledge in the system ontology), can improve the analysis of search queries, because it enables a flexible identification of the topics the user is searching for, regardless of the adopted vocabulary.
While the strength of Topological Data Analysis has been explored in many studies on high dimensional numeric data, it is still a challenging task to apply it to text.
Applications such as machine translation, speech recognition, and information retrieval require efficient handling of noun compounds as they are one of the possible sources for out-of-vocabulary (OOV) words.
However, existing networks do not make full use of syntactic features, which play a fundamental role in capturing very long-range dependencies.
For an arbitrary collection of assortments, our algorithms can find a solution in time that is sub-linear in the number of assortments and for the simpler case of cardinality constraints - linear in the number of items (existing methods are quadratic or worse).
Graph comparison is a fundamental operation in data mining and information retrieval.
The difference in performance of these systems with and without using the stemmer is analysed.