168 papers with code ยท
Natural Language Processing

Information retrieval is the task of ranking a list of documents or search results in response to a query

( Image credit: sudhanshumittal )

We consider the large-scale query-document retrieval problem: given a query (e. g., a question), return the set of relevant documents (e. g., paragraphs containing the answer) from a large document corpus.

Extraction of symbolic information from signals is an active field of research enabling numerous applications especially in the Musical Information Retrieval domain.

The contributions of our presented research are as follows: (1) we present the first distributional analysis of mathematical formulae on arXiv and zbMATH; (2) we retrieve relevant mathematical objects for given textual search queries (e. g., linking $P_{n}^{(\alpha, \beta)}\!\left(x\right)$ with `Jacobi polynomial'); (3) we extend zbMATH's search engine by providing relevant mathematical formulae; and (4) we exemplify the applicability of the results by presenting auto-completion for math inputs as the first contribution to math recommendation systems.

Text retrieval involves searching and ranking of text documents for a given set of query terms.

Our experiments using text classification and document retrieval confirm the above by comparing SEA (and a boundless variant called BSEA) to online and offline learning methods for contextual bandit problems.

INFORMATION RETRIEVAL LEARNING-TO-RANK MULTI-ARMED BANDITS SAFE EXPLORATION TEXT CLASSIFICATION

Instead of computing $k$ independent Gumbel random variables directly, we find that there exists a technique to generate these variables in descending order.

In a further consequence it can be concluded that this visual information is music related and thus should be beneficial for the corresponding MIR tasks such as music genre classification or mood recognition.

Clustering analysis has become a ubiquitous information retrieval tool in a wide range of domains, but a more automatic framework is still lacking.

The Bibliometric-enhanced Information Retrieval workshop series (BIR) was launched at ECIR in 2014 \cite{MayrEtAl2014} and it was held at ECIR each year since then.