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This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair.
Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling technique that uses kernels to extract multi-level soft match features, and a learning-to-rank layer that combines those features into the final ranking score.
This paper concerns a deep learning approach to relevance ranking in information retrieval (IR).
We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016).
#4 best model for Ad-Hoc Information Retrieval on TREC Robust04
Neural networks provide new possibilities to automatically learn complex language patterns and query-document relations.
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 semantic space.
Following recent successes in applying BERT to question answering, we explore simple applications to ad hoc document retrieval.