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Deep learning modelling techniques: current progress, applications, advantages, and challenges
Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
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 …
Specter: Document-level representation learning using citation-informed transformers
Representation learning is a critical ingredient for natural language processing systems.
Recent Transformer language models like BERT learn powerful textual representations, but …
Recent Transformer language models like BERT learn powerful textual representations, but …
A deep look into neural ranking models for information retrieval
Ranking models lie at the heart of research on information retrieval (IR). During the past
decades, different techniques have been proposed for constructing ranking models, from …
decades, different techniques have been proposed for constructing ranking models, from …
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 …
[HTML][HTML] Dataset search: a survey
Generating value from data requires the ability to find, access and make sense of datasets.
There are many efforts underway to encourage data sharing and reuse, from scientific …
There are many efforts underway to encourage data sharing and reuse, from scientific …
Pre-training methods in information retrieval
The core of information retrieval (IR) is to identify relevant information from large-scale
resources and return it as a ranked list to respond to user's information need. In recent years …
resources and return it as a ranked list to respond to user's information need. In recent years …
From neural re-ranking to neural ranking: Learning a sparse representation for inverted indexing
The availability of massive data and computing power allowing for effective data driven
neural approaches is having a major impact on machine learning and information retrieval …
neural approaches is having a major impact on machine learning and information retrieval …
Zero-shot neural passage retrieval via domain-targeted synthetic question generation
A major obstacle to the wide-spread adoption of neural retrieval models is that they require
large supervised training sets to surpass traditional term-based techniques, which are …
large supervised training sets to surpass traditional term-based techniques, which are …
Knowledge graphs: An information retrieval perspective
In this survey, we provide an overview of the literature on knowledge graphs (KGs) in the
context of information retrieval (IR). Modern IR systems can benefit from information …
context of information retrieval (IR). Modern IR systems can benefit from information …