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Journal articles on the topic 'NICUDAMAS (Information retrieval system)'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Kumar, Ram, and S. C. Sharma. "Information Retrieval System." International Journal of Technology Diffusion 9, no. 1 (January 2018): 1–10. http://dx.doi.org/10.4018/ijtd.2018010101.

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Information Retrieval Systems (IRS) have dramatically changed the ways how people acquire information for their need. Information Retrieval (IR) enables user to find relevant document from collection of countless resources. This article presents an overview of IRS. Objectives of this article is to answer all the basic and specific questions related to IRS. In contrast to other review papers, the authors provide a complete understanding of IR in single paper. Starting from definition and importance it covers retrieval process, performance issues, and comparison among various approaches. This article also includes description of different models along with analysis of their merits and demerits. This article proposes a list of challenges, still unanswered by existing systems. Before offering a conclusion, the major applications of IR are also listed.

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Abimbola, Bola. "Intelligent Information Retrieval System." International Journal of Artificial Intelligence and Machine Learning 2, no. 1 (January 18, 2022): 71. http://dx.doi.org/10.51483/ijaiml.2.1.2022.71-74.

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3

Choi, Okkyung, Kangseok Kim, Duksang Wang, Hongjin Yeh, and Manpyo Hong. "Personalized Mobile Information Retrieval System." International Journal of Advanced Robotic Systems 9, no. 1 (January 1, 2012): 11. http://dx.doi.org/10.5772/50910.

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Building a global Network Relations with the internet has made huge changes in personal information system and even comments left on a webpage of SNS(Social Network Services) are appreciated as important elements that would provide valuable information for someone. Social Network is a relation between individuals or groups, represented in a graph model, which converts the concept of psychological and social relations into a logical structure by using node and link. But, most of the current personalized systems on the basis of Social Network are built and constructed mainly in the PC environment, and the systems are neither designed nor implemented in mobile environment. Hence, the objective of this study is to propose methods of providing Personalized Mobile Information Retrieval System using NFC (Near Field Communication) Smartphone, which will be then used for Smartphone users. Besides, this study aims to verify its efficiency through a comparative analysis of existing studies.

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Hong, Seok-Joo, and Young-Bae Park. "Information Retrieval System for R2SS." Journal of the Korea Contents Association 9, no. 12 (December 28, 2009): 39–51. http://dx.doi.org/10.5392/jkca.2009.9.12.039.

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5

Md. Kowsher, Imran Hossen, and SkShohorab Ahmed. "Bengali Information Retrieval System(BIRS)." International Journal on Natural Language Computing 8, no. 5 (October 31, 2019): 1–12. http://dx.doi.org/10.5121/ijnlc.2019.8501.

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Tewari, Ashish Kumar, Rashi, Gulshan Wadhwa, Sanjeev Kumar Sharma, and Chakresh Kumar Jain. "BIRS – Bioterrorism Information Retrieval System." Bioinformation 9, no. 2 (January 18, 2013): 112–15. http://dx.doi.org/10.6026/97320630009112.

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7

SHIRAHASHI, Akihiro. "Information retrieval system through Internet." Journal of Information Processing and Management 37, no. 1 (1994): 19–26. http://dx.doi.org/10.1241/johokanri.37.19.

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8

Davcev, Danco, Dusan Cakmakov, and Vanco Cabukovski. "Distributed multimedia information retrieval system." Computer Communications 15, no. 3 (April 1992): 177–84. http://dx.doi.org/10.1016/0140-3664(92)90078-s.

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9

Chkiwa, Mounira, Anis Jedidi, and Faiez Gargouri. "Semantic / Fuzzy Information Retrieval System." International Journal of Information Technology and Web Engineering 12, no. 1 (January 2017): 37–56. http://dx.doi.org/10.4018/ijitwe.2017010103.

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In this paper, the authors present an overall description of their information retrieval system which makes a practical collaboration between Semantic Web and Fuzzy logic in order to have profit from their advantages in the information retrieval domain. Their system is dedicated for kids, for this reason the semantic/fuzzy collaboration materialized must be in the background of the information retrieval process because such category of users cannot certainly control semantic web technologies neither fuzzy logic commands. In this paper, the authors present the different services proposed by their system and how they use Semantic Web and Fuzzy logic to develop it. Evaluation tests of the system using universal measures show clearly its efficiency.

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10

Gudi, Anirudh. "Hypertext Based Information Retrieval System." IETE Technical Review 12, no. 4 (July 1995): 261–67. http://dx.doi.org/10.1080/02564602.1995.11416538.

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11

Lucarella, D., and R. Morara. "FIRST: Fuzzy Information Retrieval SysTem." Journal of Information Science 17, no. 2 (April 1991): 81–91. http://dx.doi.org/10.1177/016555159101700202.

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12

Zelitchenko, Alexander I. "Information retrieval expert system “matchmaker”." Computers in Human Behavior 8, no. 4 (December 1992): 281–96. http://dx.doi.org/10.1016/0747-5632(92)90025-a.

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13

Escobedo, Richard, Scott D. G. Smith, and Thomas P. Caudell. "A neural information retrieval system." International Journal of Advanced Manufacturing Technology 8, no. 4 (July 1993): 269–73. http://dx.doi.org/10.1007/bf01748637.

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14

Hiromichi, F. "Knowledge based information retrieval system." Expert Systems with Applications 9, no. 3 (1995): IV—V. http://dx.doi.org/10.1016/0957-4174(95)98747-7.

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15

YOKOYAMA, Kiyoshi, Hidenori MURAKAMI, and Nobuyuki FUKASAWA. "Toward intelligent information system. (4). Expert system for information retrieval." Journal of Information Processing and Management 32, no. 3 (1989): 223–34. http://dx.doi.org/10.1241/johokanri.32.223.

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16

Bovey, J. D. "A graphical retrieval system." Journal of Information Science 19, no. 3 (June 1993): 179–87. http://dx.doi.org/10.1177/016555159301900302.

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17

McWhirr, Alan. "GRS (General Retrieval System): a retrieval system for use in the humanities." Program 20, no. 3 (March 1986): 275–88. http://dx.doi.org/10.1108/eb046942.

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18

Bedi, P., and S. Chawla. "Agent Based Information Retrieval System Using Information Scent." Journal of Artificial Intelligence 3, no. 4 (September 15, 2010): 220–38. http://dx.doi.org/10.3923/jai.2010.220.238.

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19

Niki, Kazuhisa. "Self-organized Information Retrieval System: SIR." Brain & Neural Networks 1, no. 1 (1994): 27–34. http://dx.doi.org/10.3902/jnns.1.27.

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20

Kim, Jae-Hoon, and Hyung-Chul Kim. "Information Retrieval System for Mobile Devices." Journal of the Korean Society of Marine Engineering 33, no. 4 (May 31, 2009): 569–77. http://dx.doi.org/10.5916/jkosme.2009.33.4.569.

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21

ZAKI, U., M. MEMON, D. N. HAKRO, K. U. R. KHOUMBATI, M. HAMEED,, M. A. ZAKI, and G. NABI. "Implementation Challenges in Information Retrieval System." SINDH UNIVERSITY RESEARCH JOURNAL -SCIENCE SERIES 51, no. 02 (June 22, 2019): 339–44. http://dx.doi.org/10.26692/sujo/2019.6.55.

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22

Angdresey, Apriandy, Miguel Angelo Lamongi, and Rinaldi Munir. "Information Retrieval System in the Bible." CogITo Smart Journal 7, no. 1 (June 30, 2021): 111. http://dx.doi.org/10.31154/cogito.v7i1.300.111-120.

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Information retrieval is used to search for relevant documents so that they can be obtained quickly and precisely. There are many Christians who want to study the gospel. However, often experience problems in finding the Gospel verse and topics dealing with the need to search by the user. Therefore, have to search individually, each verse in the four Gospels to find the topic or verse that the user wants to find out. In this study, the authors used the Bible verses in the Gospels as documents, so that these verses could be searched for the level of relevance or similarity to the entered keywords. Furthermore, to determine the level of relevance between documents and keywords is calculated using the Vector Space Model. Based on the application that has been successfully built, the application can be show 10 documents related to the keywords that are searched and sorted from the most relevant, with the highest similarity value, namely 78.65%.Keywords - Information Retrieval, Vector Space Model, Bible.

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23

Gao, Ruoyuan. "Toward a fairer information retrieval system." ACM SIGIR Forum 55, no. 1 (June 2021): 1–2. http://dx.doi.org/10.1145/3476415.3476429.

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With the increasing popularity and social influence of information retrieval (IR) systems, various studies have raised concerns on the presence of bias in IR and the social responsibilities of IR systems. Techniques for addressing these issues can be classified into pre-processing , in-processing and post-processing. Pre-processing reduces bias in the data that is fed into machine learning models. In-processing encodes fairness constraints as a part of the objective function or learning process. Post-processing operates as a top layer over the trained model to reduce the presentation bias exposed to users. This dissertation explored ways to bring the pre-processing and post-processing approaches, together with the fairness-aware evaluation metrics, into a unified framework as an attempt to break the vicious cycle of bias and improve fairness in IR. We first investigated the existing bias presented in search engine results. Specifically, we focused on the top-k fairness ranking in terms of statistical parity fairness and disparate impact fairness definitions. With Google search and a general purposed text cluster as a lens, we explored several topical diversity fairness ranking strategies to understand the relationship between relevance and fairness in search results. Our experimental results showed that different fairness ranking strategies resulted in distinct utility scores and performed differently with distinct datasets. Second, to further investigate the relationship of data and fairness algorithms, we developed a statistical framework that was able to facilitate various analysis and decision making. Our framework could effectively and efficiently estimate the domain of data and solution space. We derived theoretical expressions to identify the fairness and relevance bounds for data of different distributions, and applied them to both synthetic datasets and real world datasets. We presented a series of use cases to demonstrate how our framework was applied to associate data and provide insights to fairness optimization problems. Third, we proposed an evaluation metric FAIR for the ranking results that encoded fairness, diversity, novelty and relevance. This metric offered a new perspective of evaluating fairness-aware ranking results. Based on this metric, we developed an effective ranking algorithm that jointly optimized for fairness and utility. Our experiments showed that our new metric was able to highlight results that achieved good user utility and fair information exposure at the same time. We showed how FAIR metric related to existing metrics through correlation analysis and case studies, and demonstrated the effectiveness of our FAIR-based algorithm.

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Lata, Suman, and Ajmer Kundu. "Wheat Production Information Retrieval System (WPIRS)." International Journal of Computer Applications 58, no. 2 (November 15, 2012): 16–19. http://dx.doi.org/10.5120/9253-3422.

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25

Kulkarni, D. R., and R. R. Bharucha. "READFAST — an online information retrieval system." Program 20, no. 3 (March 1986): 314–19. http://dx.doi.org/10.1108/eb046945.

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26

Ertwine, Robyn, and Beatrice Jakubowski. "CHID: An Automated Information Retrieval System." Diabetes Educator 12, no. 1 (January 1986): 55–60. http://dx.doi.org/10.1177/014572178601200114.

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The Combined Health Information Database (CHID) is an automated information system designed for health care educators and other health professionals. It contains 20,000 references to jour nal articles, fact sheets, brochures, audiovisual materials, health program descriptions, and other health-related information. Topics include diabetes, arthritis, digestive diseas es, high blood pressure, and health promotion and health education. Access to this information is available nationwide through a subscription service. The information included in CHID can be searched and retrieved quickly by computer, and the results can be orga nized and prepared according to the specific needs of the user.

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27

Shu-dong, Shi, and Li Zhi-tang. "A high-speed information retrieval system." Wuhan University Journal of Natural Sciences 9, no. 4 (July 2004): 425–28. http://dx.doi.org/10.1007/bf02830436.

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28

Li, Li, Wei Jiang Li, and Yuan Yuan Fan. "Topic-Oriented Information Retrieval." Advanced Materials Research 989-994 (July 2014): 4845–50. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4845.

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With the rapid development of Internet, more and more information has been displayed to people. More and more researcher pay more attention to how to find useful information from this huge information ocean. We design the topic-oriented information retrieval system in order to overcome the drawback of generic crawler. The system retrieves topic information efficiently and helps user get useful information rapidly and exactly.

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Shen, Jin Xing. "Ontology-Based Semantic Retrieval for Management Information System." Applied Mechanics and Materials 278-280 (January 2013): 2069–72. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.2069.

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In order to achieve semantic retrieval for scientific research information in WWW, this paper applies an ontology-based framework to information retrieval system for management information system. After analyze the limitations of traditional method, bring a semantic search forward, and mainly introduce the thought of the semantic retrieval as well as the way to constitute ontology entity and the language that describes it. Moreover, semantic retrieval system based on ontology is also given. The application to retrieve project information shows that the framework can overcome the localization of other ontology’s models, and this research facilitates the semantic retrieval of management information through semantic retrieval concepts on the Semantic Web.

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30

Kovalcik, Justin, and Mike Villalobos. "Automated Storage & Retrieval System." Information Technology and Libraries 38, no. 4 (December 16, 2019): 114–24. http://dx.doi.org/10.6017/ital.v38i4.11273.

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The California State University, Northridge (CSUN) Oviatt Library was the first library in the world to integrate an automated storage and retrieval system (AS/RS) into its operations. The AS/RS continues to provide efficient space management for the library. However, added value has been identified in materials security and inventory as well as customer service. The concept of library as space, paired with improved services and efficiencies, has resulted in the AS/RS becoming a critical component of library operations and future strategy. Staffing, service, and security opportunities paired with support and maintenance challenges, enable the library to provide a unique critique and assessment of an AS/RS.

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31

Jones, Kevin P., and Colin L. M. Bell. "MORPHS—an intelligent retrieval system." Aslib Proceedings 38, no. 3 (March 1986): 71–79. http://dx.doi.org/10.1108/eb051000.

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32

Zhang, Shu Dong, and Yan Chen. "Research on Domain Ontology-Based Intelligent Information Retrieval System." Key Engineering Materials 460-461 (January 2011): 300–304. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.300.

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Domain ontology introduces a new theory and method for information retrieval. In this paper, we analyze the deficiencies of traditional information retrieval and explore the relationship between domain ontology and information retrieval, as well as the basic design ideas of information retrieval based on domain ontology. Finally we present a domain ontology-based intelligent information retrieval system, so that the information retrieval can be promoted from the keyword level to the semantic level. With the rapid development of the national economy and the growth of information resources, traditional methods relying on the browser, database fields, keyword matching, or even manual retrieval query has become increasingly difficult to meet people's information retrieval needs. How to quickly and accurately identify the needed information resources has become a urgent question in front of us. Information retrieval is a technology which can find out the relevant information the user needs from a collection of large amounts of information. It has experienced manual retrieval, computer retrieval stage, now it has developed to the network and intelligent stage. The objects of information retrieval extend from a relative closed, stable and consistent, centrally managed information content by an independent database to an open, dynamic, quickly update, widely distributed, and loosely managed web content; the users of information retrieval also spread from professional intelligence agent to the common including government officials, businessmen, managers, teachers, students, professionals, etc. They ask for the higher and more diverse requirements from the results to the manner of information retrieval. Adapting to the need for network, intelligence and personalization is a new trend of information retrieval technology.

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33

Sen, Procheta. "Proactive information retrieval." ACM SIGIR Forum 55, no. 2 (December 2021): 1–2. http://dx.doi.org/10.1145/3527546.3527576.

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Users interact with digital systems with some underlying tasks in their minds. In our research scope, a task can be either single or multi-staged. A single-staged task is associated with a single information need, whereas a multi-staged one is associated with more than one information needs. An example of a single-staged task is suggesting related papers to an author while they are writing a section of the research papers. An example of a multi-staged task is planning for a vacation, where the different underlying information needs could be 'places to visit', 'booking accommodation' etc. In the process of accomplishing their task objectives, a user often needs to interact with an information retrieval (IR) system to address one or more information needs. For instance, for writing a research paper on a chosen topic, a user needs to look for existing research work related to the topic. Traditional IR systems do not take into account a user's task intent while showing search results to the user. In our work, we propose a methodology towards developing next generation IR systems (i.e. proactive IR systems) which seek to anticipate the task intent of a user from their interactions with digital systems in order to proactively suggest potentially relevant information sources to assist them to complete their tasks. Specifically speaking, in this PhD, we proposed an embedding approach that captures the task semantics from the interactions of a user with a digital system (e.g. laptop, desktop, smartphone etc.). The proposed embedding approach is then applied for the downstream tasks of providing proactive suggestions in both single and multi staged scenarios. For the single-staged task, we propose a simulation setup to simulate a user's reading and writing interactions in a desktop environment. For the multi-staged task, we focus on web search sessions where a user can have multiple information needs corresponding to a search task. We also proposed a reproducible evaluation framework to compare between different proactive suggestion models. Awarded by : Dublin City University, Ireland on 10 August 2021. Supervised by : Gareth Jones. Available at : https://procheta.github.io/sprocheta/Thesis.pdf.

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Lokhande, Kalyani, and Dhanashree Tayade. "English-Marathi Cross Language Information Retrieval System." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 112. http://dx.doi.org/10.23956/ijarcsse.v7i8.34.

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Nowadays, diﬀerent types of content in diﬀerent languages are available on World Wide Web and their usage is increasing rapidly. Cross Language Information Retrieval (CLIR) deals with retrieval of documents in another language than the language of the requested query. Various researchers worked on Cross Language Information Retrieval systems for Indian languages using diﬀerent translation approaches. There is still CLIR system to be developed which allow user to retrieve Marathi documents when English query is given. In the proposed English to Marathi Cross Language Information Retrieval system, translation is based on query translation approach. The proposed system retrieves Marathi documents depending on matching terms in query. The performance of the proposed system is improved by query pre-processing and query expansion using WordNet.

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35

Beitzel, Steven M., Eric C. Jensen, Abdur Chowdhury, David Grossman, Ophir Frieder, and Nazli Goharian. "Fusion of effective retrieval strategies in the same information retrieval system." Journal of the American Society for Information Science and Technology 55, no. 10 (2004): 859–68. http://dx.doi.org/10.1002/asi.20012.

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36

Liu, Na, and Kun Liu. "Mobile Agents Build Web Information Retrieval System." Applied Mechanics and Materials 543-547 (March 2014): 3373–76. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.3373.

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In recent years, mobile agent has always been the hot spot of the academic research, this paper introduces the concept of mobile agent, mobile agent system architecture and key technologies, combined with the mobile technology and web information retrieval technology, design a model of information retrieval system based on mobile agent, and expounds the key technology to realize the model needs to solve.

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37

KumarKatiyar, Vijay, and Dalip Kamboj. "TIRS: SMS based Transportation Information Retrieval System." International Journal of Computer Applications 76, no. 12 (August 23, 2013): 15–19. http://dx.doi.org/10.5120/13299-0757.

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38

Radhouani, Saïd, Claire-Lise Mottaz Jiang, and Gilles Falquet. "FlexIR: a Domain-Specific Information Retrieval System." Polibits 39 (June 30, 2009): 27–31. http://dx.doi.org/10.17562/pb-39-4.

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39

T, Jyothi. "Efficient Information Retrieval System for Cloud Environments." IJARCCE 6, no. 4 (April 30, 2017): 312–15. http://dx.doi.org/10.17148/ijarcce.2017.6459.

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40

Zahra, Syeda Binish. "Desktop Based: Off-line Information Retrieval System." International Journal for Electronic Crime Investigation 2, no. 4 (December 7, 2018): 4. http://dx.doi.org/10.54692/ijeci.2018.020423.

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Information retrieval is rapidlydeveloping field and there are many changes are introduced day by day in traditional techniques for IR. IR system is intended to evaluate examine and accumulate the sources of information and get back those that match user's requirement. The need of today's fast moving life is to get maximum information but within minimum time. For getting maximum information in minimum time requires more efforts. The main functionality of IR is to provide access of documents (that document may be in collection of thousand, or millions). With the help of an appropriate description, user can recover any one document from a collection of documents. In this paper I describe my IR system which retrieves information from any directory and this information may be in terms of image, audio or in text form. The selection of good features also allows the space, time and costs of the retrieval process to be reduced. Two documents may be considered similar in this system if they have same name and places in different folders or directories. To explore the retrieval process from that system, I used Apache Lucene with JAVA implemented in IntelliJ.

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Zahra, Syeda Binish. "Desktop Based: Off-line Information Retrieval System." International Journal for Electronic Crime Investigation 2, no. 4 (December 7, 2018): 4. http://dx.doi.org/10.54692/ijeci.2019.030123.

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Information retrieval is rapidlydeveloping field and there are many changes are introduced day by day in traditional techniques for IR. IR system is intended to evaluate examine and accumulate the sources of information and get back those that match user's requirement. The need of today's fast moving life is to get maximum information but within minimum time. For getting maximum information in minimum time requires more efforts. The main functionality of IR is to provide access of documents (that document may be in collection of thousand, or millions). With the help of an appropriate description, user can recover any one document from a collection of documents. In this paper I describe my IR system which retrieves information from any directory and this information may be in terms of image, audio or in text form. The selection of good features also allows the space, time and costs of the retrieval process to be reduced. Two documents may be considered similar in this system if they have same name and places in different folders or directories. To explore the retrieval process from that system, I used Apache Lucene with JAVA implemented in IntelliJ.

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42

Davis, G. L., Edward F. Gilman, and Howard W. Beck. "An Electronically Based Horticultural Information Retrieval System." HortTechnology 6, no. 4 (October 1996): 332–36. http://dx.doi.org/10.21273/horttech.6.4.322.

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A large horticultural database and an electronic retrieval system for extension education programs were developed using compact disk-read only memory (CD-ROM) and World Wide Web (WWW) as the medium for information delivery. Object-oriented database techniques were used to organize the information. Conventional retrieval techniques including hypertext, full text searching, and expert systems were integrated into a complete package for accessing information stored in the database. A multimedia user interface was developed to provide a variety of capabilities including computer graphics and high resolution digitized images. Information for the CD-ROM was gathered from extension publications that were tagged using the standard generalized markup language (SGML)-based document markup language (International Standards Organization, 1986). Combining funds from the state legislator with grants from the USDA and other institutions, the CD-ROM system has been implemented in all 67 county extension offices in Florida and is available to the public as a for-sale CD-ROM. Public access is also available to most of the database through the WWW.

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43

Swathi, J., and G. Seethalakshmi. "Content and Location based Information Retrieval System." International Journal of Computer Applications 107, no. 11 (December 18, 2014): 1–4. http://dx.doi.org/10.5120/18792-0115.

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44

Deo, Arpit. "A SURVEY PAPER ON INFORMATION RETRIEVAL SYSTEM." International Journal of Advanced Research in Computer Science 9, no. 1 (February 20, 2018): 778–81. http://dx.doi.org/10.26483/ijarcs.v9i1.5505.

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45

Wrba, Th, G. Duftschmid, P. Sachs, W. Gall, Ch Rehnelt, G. Boldt, W. Premauer, and W. Dorda. "ArchiMed: A Medical Information and Retrieval System." Methods of Information in Medicine 38, no. 01 (1999): 16–24. http://dx.doi.org/10.1055/s-0038-1634148.

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Abstract:ArchiMed is a highly flexible medical data storage and retrieval system which adds sophisticated clinical research support to a standard hospital information system (HIS).Currently, the HIS of Vienna General Hospital-University Hospital (2000 beds) stores the clinical data of over 2 million patients. While this system supports patient care (e.g., ADT, clinical chemistry, diagnosis, procedures), it has no features to facilitate research, such as the management of clinical studies.ArchiMed is designed to support clinical research. It includes an independent database, which mirrors virtually all the information held in the HIS while also allowing new data to be collected independently and to be added to the database. Flexible retrieval and analysis of data contained in the database are then possible. Thus, existing patient data can be smoothly incorporated into a study together with data collected specifically for research purposes. The system has already been successfully installed in the departments of surgery and soon in other departments as well.

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BAGGIA, P., L. FISSORE, E. GIACHIN, G. MICCA, C. RULLENT, and P. LAFACE. "A SPEECH UNDERSTANDING SYSTEM FOR INFORMATION RETRIEVAL." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 01 (February 1994): 71–97. http://dx.doi.org/10.1142/s0218001494000048.

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This paper describes a Continuous Speech Understanding System that allows information services to be accessed through the telephone line. It accepts queries within a restricted semantic domain, expressed in free but syntactically correct natural language, with a lexicon of the order of 800 words. In the implementation here described, a user can access an electronic mailbox or a train information service through a PABX telephone line. The architecture of the system is based on two main modules that represent and use different knowledge sources. A speaker independent recognition module generates, for each utterance, a lattice of word hypotheses which is the interface to an understanding module that performs the syntactic and semantic analysis. The recognition module is based on Hidden Markov Models of subword units, and performs the acoustic decoding process according to a beam search strategy. The understanding module finds the most likely sequence of words and represents its meaning in a format which facilitates the access to a database. It makes use of a modified caseframe analysis guided by the word hypotheses scores. Experiments were performed with 600 sentences from 10 speakers on the E-Mail application task. Using 15 Gaussian mixtures per state, a word accuracy of 75.7 was obtained with a test vocabulary of 787 words and no linguistic constraints. Linguistic processing of the corresponding lattices achieved a sentence understanding rate of 82%.

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Wood, Murray, and Ian Sommerville. "An information retrieval system for software components." Software Engineering Journal 3, no. 5 (1988): 198. http://dx.doi.org/10.1049/sej.1988.0025.

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Kim, Young-Geil. "The Information Retrieval System for Software Reuse." Journal of Korea Institute of Information, Electronics, and Communication Technology 9, no. 1 (February 29, 2016): 1–8. http://dx.doi.org/10.17661/jkiiect.2016.9.1.001.

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Myna, A. N., K. Deepthi, and Samvrudhi V. Shankar. "Hybrid Recommender System for Music Information Retrieval." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4145–49. http://dx.doi.org/10.1166/jctn.2020.9035.

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Music plays an integral role in our lives as the most popular type of recreation. With the advent of new technologies such as Internet and portable media players, large amount of music data is available online which can be distributed and easily made available to people. Enormous amount of music data is released every year by several artists with songs varying in features, genre and so on. Because of this, a need for reliable and easy access of songs based on user preferences is necessary. The recommender system focuses on generating playlists based on the physical, perceptual and acoustical properties of the song (content based filtering approach), or on commonalities between users on a particular basis like ratings or user data history (collaborative filtering). The system thus developed is a hybrid music recommender tool which creates a user centric suggestion system accompanied by feature extraction which in turn enhances the accuracy of music recommendations.

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Bender, Avi. "An Optical Disk‐Based Information Retrieval System." Library Hi Tech 6, no. 3 (March 1988): 81–85. http://dx.doi.org/10.1108/eb047734.

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