Information retrieval is the process of finding relevant information from a collection of documents or data sources in response to a user's query. This article explores recent advancements, challenges, and practical applications in the field of information retrieval.
Information retrieval has evolved significantly with the introduction of machine learning techniques and the increasing availability of data. Researchers have been developing various approaches to improve the effectiveness and efficiency of information retrieval systems. Some of these approaches include content-and-structure retrieval, dense retrieval, adversarial information retrieval, and explainable information retrieval.
Recent research in the field has focused on enhancing retrieval systems by utilizing native XML databases, dense phrase retrieval, and modular retrieval. These methods aim to improve the retrieval process by considering the structure and content of documents, fine-grained retrieval units, and the composition of multiple existing retrieval modules.
One of the main challenges in information retrieval is the trade-off between efficiency and effectiveness. Dense retrieval methods, which use pre-trained transformer models, have shown significant improvements in retrieval effectiveness but are computationally intensive. To address this issue, researchers have proposed hybrid retrieval systems that combine the benefits of both sparse and dense retrieval methods.
Practical applications of information retrieval can be found in various domains, such as legal case retrieval, multimedia information retrieval, and music information retrieval. For instance, in legal case retrieval, researchers have demonstrated the effectiveness of combining lexical and dense retrieval methods on the paragraph-level of cases. In multimedia information retrieval, content-based methods allow retrieval based on inherent characteristics of multimedia objects, such as visual features or spatial relationships. In music information retrieval, computational methods have been developed for the visual display and analysis of music information.
One company case study in the field of information retrieval is the Competition on Legal Information Extraction/Entailment (COLIEE), which evaluates retrieval methods for the legal domain. The competition has shown that combining BM25 and dense passage retrieval using domain-specific embeddings can yield improved results.
In conclusion, information retrieval is a rapidly evolving field with numerous advancements and challenges. By leveraging machine learning techniques and addressing the trade-offs between efficiency and effectiveness, researchers are developing innovative solutions to improve the retrieval process and its applications across various domains.
Information retrieval
Information retrieval Further Reading
1.Enhancing Content-And-Structure Information Retrieval using a Native XML Database http://arxiv.org/abs/cs/0508017v1 Jovan Pehcevski, James A. Thom, Anne-Marie Vercoustre2.Phrase Retrieval Learns Passage Retrieval, Too http://arxiv.org/abs/2109.08133v1 Jinhyuk Lee, Alexander Wettig, Danqi Chen3.A Survey on Adversarial Information Retrieval on the Web http://arxiv.org/abs/1911.11060v3 Saad Farooq4.Explainable Information Retrieval: A Survey http://arxiv.org/abs/2211.02405v1 Avishek Anand, Lijun Lyu, Maximilian Idahl, Yumeng Wang, Jonas Wallat, Zijian Zhang5.Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse Retrieval Strategy Selection http://arxiv.org/abs/2109.10739v1 Negar Arabzadeh, Xinyi Yan, Charles L. A. Clarke6.Content Based Multimedia Information Retrieval to Support Digital Libraries http://arxiv.org/abs/1207.4259v1 Mohammad Nabil Almunawar7.Modular Retrieval for Generalization and Interpretation http://arxiv.org/abs/2303.13419v1 Juhao Liang, Chen Zhang, Zhengyang Tang, Jie Fu, Dawei Song, Benyou Wang8.DoSSIER@COLIEE 2021: Leveraging dense retrieval and summarization-based re-ranking for case law retrieval http://arxiv.org/abs/2108.03937v1 Sophia Althammer, Arian Askari, Suzan Verberne, Allan Hanbury9.Visual Display and Retrieval of Music Information http://arxiv.org/abs/1807.10204v1 Rafael Valle10.PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval http://arxiv.org/abs/2201.01614v2 Sophia Althammer, Sebastian Hofstätter, Mete Sertkan, Suzan Verberne, Allan HanburyInformation retrieval Frequently Asked Questions
What is information retrieval with example?
Information retrieval is the process of finding relevant information from a collection of documents or data sources in response to a user's query. For example, when you search for a specific topic on a search engine like Google, the search engine uses information retrieval techniques to find and display the most relevant web pages related to your query.
What is information retrieval used for?
Information retrieval is used for various purposes, such as searching for relevant documents, filtering and organizing information, and discovering patterns or trends in large datasets. It is widely used in search engines, recommendation systems, digital libraries, and other applications where users need to find relevant information quickly and efficiently.
What are 2 examples of information retrieval systems?
Two examples of information retrieval systems are: 1. Search engines like Google, Bing, and Yahoo, which help users find relevant web pages based on their search queries. 2. Digital libraries like PubMed and arXiv, which allow researchers to search for and access scientific articles and papers related to their field of study.
What are the three types of information retrieval?
The three types of information retrieval are: 1. Content-based retrieval: This type focuses on finding documents or items based on their content, such as keywords, phrases, or topics. 2. Structure-based retrieval: This type considers the structure of documents or data sources, such as headings, sections, or links, to improve the retrieval process. 3. Hybrid retrieval: This type combines content-based and structure-based retrieval methods to enhance the effectiveness and efficiency of information retrieval systems.
What are recent advancements in information retrieval?
Recent advancements in information retrieval include the development of new approaches like content-and-structure retrieval, dense retrieval, adversarial information retrieval, and explainable information retrieval. These methods aim to improve the retrieval process by considering the structure and content of documents, fine-grained retrieval units, and the composition of multiple existing retrieval modules.
What are the challenges in information retrieval?
One of the main challenges in information retrieval is the trade-off between efficiency and effectiveness. Dense retrieval methods, which use pre-trained transformer models, have shown significant improvements in retrieval effectiveness but are computationally intensive. To address this issue, researchers have proposed hybrid retrieval systems that combine the benefits of both sparse and dense retrieval methods.
How is machine learning used in information retrieval?
Machine learning is used in information retrieval to develop algorithms and models that can learn from data and improve the retrieval process. These techniques can help in ranking documents, understanding user queries, and personalizing search results based on user preferences and behavior. Machine learning can also be used to develop content-based, structure-based, and hybrid retrieval methods that enhance the effectiveness and efficiency of information retrieval systems.
What are some practical applications of information retrieval?
Practical applications of information retrieval can be found in various domains, such as legal case retrieval, multimedia information retrieval, and music information retrieval. In legal case retrieval, researchers have demonstrated the effectiveness of combining lexical and dense retrieval methods on the paragraph-level of cases. In multimedia information retrieval, content-based methods allow retrieval based on inherent characteristics of multimedia objects, such as visual features or spatial relationships. In music information retrieval, computational methods have been developed for the visual display and analysis of music information.
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