Statistical language models have recently been successfully applied to many information
retrieval problems. A great deal of recent work has shown that statistical language models …

We live in an information society that strongly relies on information retrieval systems, such as
search engines and conversational assistants. Consequently, the trustworthiness of these …

Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …

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 …

In neural Information Retrieval, ongoing research is directed towards improving the first
retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using …

In recent years, deep neural networks have led to exciting breakthroughs in speech
recognition, computer vision, and natural language processing (NLP) tasks. However, there …

Clustering is a widely studied data mining problem in the text domains. The problem finds
numerous applications in customer segmentation, classification, collaborative filtering …

This paper concerns a deep learning approach to relevance ranking in information retrieval
(IR). Existing deep IR models such as DSSM and CDSSM directly apply neural networks to …

Recent years have seen a dramatic growth of natural language text data, including web
pages, news articles, scientific literature, emails, enterprise documents, and social media …

Locating bugs is important, difficult, and expensive, particularly for large-scale systems. To
address this, natural language information retrieval techniques are increasingly being used …