Diagnosis prediction, which aims to predict future health information of patients from
historical electronic health records (EHRs), is a core research task in personalized …

Document Information retrieval consists of finding the documents in a collection of
documents that are the most relevant to a user query. Information retrieval techniques are …

This paper addresses the problem of responding to user queries by fetching the most
relevant object from a clustered set of objects. It addresses the common drawbacks of cluster …

The query-performance prediction task has been described as estimating retrieval
effectiveness in the absence of relevance judgments. The expectations throughout the years …

Most of the existing methods for search result diversification (SRD) appeal to the greedy
strategy for generating diversified results, which is formulated as a sequential process of …

We describe a weakly-supervised method for training deep learning models for the task of
ad-hoc document retrieval. Our method is based on generative and discriminative models …

A number of retrieval models incorporating term dependencies have recently been
introduced. Most of these modify existing" bag-of-words" retrieval models by including …

Extracting valuable insights from a large volume of unstructured data such as texts through
clustering analysis is paramount to many big data applications. However, document …

According to common relevance-judgments regimes, such as TREC's, a document can be
deemed relevant to a query even if it contains a very short passage of text with pertinent …

The cluster hypothesis states that “closely associated documents tend to be relevant to the
same requests”[45]. This is one of the most fundamental and influential hypotheses in the …