29,255 research outputs found
The HyperBagGraph DataEdron: An Enriched Browsing Experience of Multimedia Datasets
Traditional verbatim browsers give back information in a linear way according
to a ranking performed by a search engine that may not be optimal for the
surfer. The latter may need to assess the pertinence of the information
retrieved, particularly when she wants to explore other facets of a
multi-facetted information space. For instance, in a multimedia dataset
different facets such as keywords, authors, publication category, organisations
and figures can be of interest. The facet simultaneous visualisation can help
to gain insights on the information retrieved and call for further searches.
Facets are co-occurence networks, modeled by HyperBag-Graphs -- families of
multisets -- and are in fact linked not only to the publication itself, but to
any chosen reference. These references allow to navigate inside the dataset and
perform visual queries. We explore here the case of scientific publications
based on Arxiv searches.Comment: Extension of the hypergraph framework shortly presented in
arXiv:1809.00164 (possible small overlaps); use the theoretical framework of
hb-graphs presented in arXiv:1809.0019
Characterizing Question Facets for Complex Answer Retrieval
Complex answer retrieval (CAR) is the process of retrieving answers to
questions that have multifaceted or nuanced answers. In this work, we present
two novel approaches for CAR based on the observation that question facets can
vary in utility: from structural (facets that can apply to many similar topics,
such as 'History') to topical (facets that are specific to the question's
topic, such as the 'Westward expansion' of the United States). We first explore
a way to incorporate facet utility into ranking models during query term score
combination. We then explore a general approach to reform the structure of
ranking models to aid in learning of facet utility in the query-document term
matching phase. When we use our techniques with a leading neural ranker on the
TREC CAR dataset, our methods rank first in the 2017 TREC CAR benchmark, and
yield up to 26% higher performance than the next best method.Comment: 4 pages; SIGIR 2018 Short Pape
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