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Matching Functions
Published in Anuradha D. Thakare, Shilpa Laddha, Ambika Pawar, Hybrid Intelligent Systems for Information Retrieval, 2023
Anuradha D. Thakare, Shilpa Laddha, Ambika Pawar
As storage device cost continues to drop, the number of databases, including relational, graphical, and textual, is increasing tremendously. Knowledge organizations provide a wide spectrum of information in huge collections of documents. These repositories are expected to expand quickly with the emergence of E-commerce and organization intranets/extranets. This exponential growth has generated large collections of documentation, fragmented and unstructured. While it is increasingly easier to gather and hold data in the document, retrieval of valuable information from these large collections of documents has become increasingly difficult. Different methods were used to solve these problems to improve recovery efficiency and in the information retrieval area, in what way the GAs can be used and by what method the matching functions (MF) adjusted with GAs.
Distributed Artificial Intelligence for Document Retrieval
Published in Satya Prakash Yadav, Dharmendra Prasad Mahato, Nguyen Thi Dieu Linh, Distributed Artificial Intelligence, 2020
Ankur Seem, Arpit Kumar Chauhan, Rijwan Khan, Satya Prakash Yadav
Document retrieval (also known as information retrieval) is a computerized user query process that searches for the most relevant data and arranges all of the relevant results in priority order. This is an evolving field that began in the 1970s. Many models for document retrieval exist. Document retrieval methods are regularly analyzed using standard tests [1]. For example, someone creates a new method for document retrieval, then applies it to real-world problems, then compares that with the existing method. The result will only be considered to be positive if it is an improvement over the existing method. The most recent and popular advancement in document retrieval applies artificial intelligence (AI) to create intelligent machines which can work and respond like humans. More recently, distributed AI (DAI), which is a combination of AI and blockchain technology, has been used in document retrieval. Using DAI, records are stored in blocks and then these blocks are linked with each other using cryptography [2,3]. Each block has a cryptographic hash for the block and the main advantage is nobody owns the data.
A survey on non-factoid question answering systems
Published in International Journal of Computers and Applications, 2022
Manvi Breja, Sanjay Kumar Jain
Document Retrieval retrieves the relevant documents to a user’s question using various techniques such as terms overlap and tf-idf scoring. Paragraph segmentation algorithm divides the ranked documents into smaller passages which are input to answer extraction module.