Deep learning models have achieved great success in solving a variety of natural language
processing (NLP) problems. An ever-growing body of research, however, illustrates the …

This paper provides an extensive and thorough overview of the models and techniques
utilized in the first and second stages of the typical information retrieval processing chain …

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …

With the development of high computational devices, deep neural networks (DNNs), in
recent years, have gained significant popularity in many Artificial Intelligence (AI) …

Entity matching (EM) finds data instances that refer to the same real-world entity. In this
paper we examine applying deep learning (DL) to EM, to understand DL's benefits and …

Ranking models lie at the heart of research on information retrieval (IR). During the past
decades, different techniques have been proposed for constructing ranking models, from …

Natural language sentence matching is a fundamental technology for a variety of tasks.
Previous approaches either match sentences from a single direction or only apply single …

Typical person re-identification (ReID) methods usually describe each pedestrian with a
single feature vector and match them in a task-specific metric space. However, the methods …

Difficulty in collecting and annotating large-scale video data raises a growing interest in
learning models which can recognize novel classes with only a few training examples. In …

When experiencing an information need, users want to engage with a domain expert, but
often turn to an information retrieval system, such as a search engine, instead. Classical …