Semantic models for the first-stage retrieval: A comprehensive review
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 …
where the first-stage retrieval is to return a subset of candidate documents and latter stages …
Information retrieval: recent advances and beyond
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 …
utilized in the first and second stages of the typical information retrieval processing chain …
Man is to computer programmer as woman is to homemaker? debiasing word embeddings
The blind application of machine learning runs the risk of amplifying biases present in data.
Such a danger is facing us with word embedding, a popular framework to represent text data …
Such a danger is facing us with word embedding, a popular framework to represent text data …
[BOOK][B] Pretrained transformers for text ranking: Bert and beyond
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 …
response to a query. Although the most common formulation of text ranking is search …
The geometry of culture: Analyzing the meanings of class through word embeddings
AC Kozlowski, M Taddy… - American Sociological …, 2019 - journals.sagepub.com
We argue word embedding models are a useful tool for the study of culture using a historical
analysis of shared understandings of social class as an empirical case. Word embeddings …
analysis of shared understandings of social class as an empirical case. Word embeddings …
End-to-end neural ad-hoc ranking with kernel pooling
This paper proposes K-NRM, a kernel based neural model for document ranking. Given a
query and a set of documents, K-NRM uses a translation matrix that models word-level …
query and a set of documents, K-NRM uses a translation matrix that models word-level …
Learning to match using local and distributed representations of text for web 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 …
distributed representations of text, and match the query against the document in the latent …
An introduction to neural information retrieval
Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …
rank search results in response to a query. Traditional learning to rank models employ …
Word2vec convolutional neural networks for classification of news articles and tweets
Big web data from sources including online news and Twitter are good resources for
investigating deep learning. However, collected news articles and tweets almost certainly …
investigating deep learning. However, collected news articles and tweets almost certainly …
Uncovering and mitigating algorithmic bias through learned latent structure
Recent research has highlighted the vulnerabilities of modern machine learning based
systems to bias, especially towards segments of society that are under-represented in …
systems to bias, especially towards segments of society that are under-represented in …