Information Retrieval in Conjunction With Deep Learning

Information is second level of abstraction after data and before knowledge. Information retrieval helps fill the gap between information and knowledge by storing, organizing, representing, maintaining, and disseminating information. Manual information retrieval leads to underutilization of resources, and it takes a long time to process, while machine learning techniques are implications of statistical models, which are flexible, adaptable, and fast to learn. Deep learning is the extension of machine learning with hierarchical levels of learning that make it suitable for complex tasks. Deep learning can be the best choice for information retrieval as it has numerous resources of information and large datasets for computation. In this chapter, the authors discuss applications of information retrieval with deep learning (e.g., web search by reducing the noise and collecting precise results, trend detection in social media analytics, anomaly detection in music datasets, and image retrieval).

• Publication year

  2020

• Venue

  Unknown venue

• Publication date

  Unknown publication date

• Fields of study

  Computer Science

• Identifiers
  DOI 10.4018/978-1-5225-9643-1.ch014
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

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• No concepts are published for this paper.

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