Information retrieval is the task of ranking a list of documents or search results in response to a query
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With the ever-growing interest in the area of mobile information retrieval and the ongoing fast development of mobile devices and, as a consequence, mobile apps, an active research area lies in studying users' behavior and search queries users submit on mobile devices.
This paper democratizes neural information retrieval to scenarios where large scale relevance training signals are not available.
While billions of non-English speaking users rely on search engines every day, the problem of ad-hoc information retrieval is rarely studied for non-English languages.
Such research will require data and tools, to allow the implementation and study of conversational systems.
Curriculum learning has recently been shown to improve neural models' effectiveness by sampling batches non-uniformly, going from easy to difficult instances during training.
In this paper we look beyond metrics-based evaluation of Information Retrieval systems, to explore the reasons behind ranking results.
Conversational search is an approach to information retrieval (IR), where users engage in a dialogue with an agent in order to satisfy their information needs.
Since most standard ad-hoc information retrieval datasets publicly available for academic research (e. g. Robust04, ClueWeb09) have at most 250 annotated queries, the recent deep learning models for information retrieval perform poorly on these datasets.
Music genre classification is one of the sub-disciplines of music information retrieval (MIR) with growing popularity among researchers, mainly due to the already open challenges.
In addition, we propose a new Tree-Edit-Distance-based Similarity (TEDS) metric for table recognition.