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 …

Text similarity measurement is the basis of natural language processing tasks, which play an
important role in information retrieval, automatic question answering, machine translation …

We enhance auto-regressive language models by conditioning on document chunks
retrieved from a large corpus, based on local similarity with preceding tokens. With a 2 …

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 …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

Multi-stage ranking pipelines have been a practical solution in modern search systems,
where the first-stage retrieval is to return a subset of candidate documents and latter stages …

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine
learning paradigm that continuously learns by accumulating past knowledge that it then …

Recent developments in representational learning for information retrieval can be organized
in a conceptual framework that establishes two pairs of contrasts: sparse vs. dense …

This paper introduces Explicit Semantic Ranking (ESR), a new ranking technique that
leverages knowledge graph embedding. Analysis of the query log from our academic search …

Models such as latent semantic analysis and those based on neural embeddings learn
distributed representations of text, and match the query against the document in the latent …