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 …

This paper presents\textttConv-KNRM, a Convolutional Kernel-based Neural Ranking Model
that models n-gram soft matches for ad-hoc search. Instead of exact matching query and …

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 …

Recently pre-trained language representation models such as BERT have shown great
success when fine-tuned on downstream tasks including information retrieval (IR). However …

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 …

This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces
knowledge graphs to neural search systems. EDRM represents queries and documents by …

Assessing relevance between a query and a document is challenging in ad-hoc retrieval
due to its diverse patterns, ie, a document could be relevant to a query as a whole or …

With the rapid development of the times, people have entered the era of intelligence, and the
application of big data algorithms is becoming increasingly widespread. The satisfaction of …

We propose the Neural Vector Space Model (NVSM), a method that learns representations
of documents in an unsupervised manner for news article retrieval. In the NVSM paradigm …

Automatic crime classification is a fundamental task in the legal field. Given the fact
descriptions, judges first determine the relevant violated laws, and then the articles. As laws …