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Journal articles on the topic 'Ad-Hoc Information Retrieval'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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1

Ormeño, Pablo, Marcelo Mendoza, and Carlos Valle. "Topic Models Ensembles for AD-HOC Information Retrieval." Information 12, no. 9 (2021): 360. http://dx.doi.org/10.3390/info12090360.

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Ad hoc information retrieval (ad hoc IR) is a challenging task consisting of ranking text documents for bag-of-words (BOW) queries. Classic approaches based on query and document text vectors use term-weighting functions to rank the documents. Some of these methods’ limitations consist of their inability to work with polysemic concepts. In addition, these methods introduce fake orthogonalities between semantically related words. To address these limitations, model-based IR approaches based on topics have been explored. Specifically, topic models based on Latent Dirichlet Allocation (LDA) allow

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Kurland, Oren, and Lillian Lee. "Clusters, language models, and ad hoc information retrieval." ACM Transactions on Information Systems 27, no. 3 (2009): 1–39. http://dx.doi.org/10.1145/1508850.1508851.

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Ensan, Faezeh, and Feras Al-Obeidat. "Relevance-based entity selection for ad hoc retrieval." Information Processing & Management 56, no. 5 (2019): 1645–66. http://dx.doi.org/10.1016/j.ipm.2019.05.005.

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Cai, Weihong, Zijun Hu, Yalan Luo, Daoyuan Liang, Yifan Feng, and Jiaxin Chen. "Multi-Layer Contextual Passage Term Embedding for Ad-Hoc Retrieval." Information 13, no. 5 (2022): 221. http://dx.doi.org/10.3390/info13050221.

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Nowadays, pre-trained language models such as Bidirectional Encoder Representations from Transformer (BERT) are becoming a basic building block in Information Retrieval tasks. Nevertheless, there are several limitations when applying BERT to the query-document matching task: (1) relevance assessments are applicable at the document-level, and the tokens of documents often exceed the maximum input length of BERT; (2) applying BERT to long documents leads to a great consumption of memory usage and run time, owing to the computational cost of the interactions between tokens. This paper explores a

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Cai, Weihong, Zijun Hu, Yalan Luo, Daoyuan Liang, Yifan Feng, and Jiaxin Chen. "Multi-Layer Contextual Passage Term Embedding for Ad-Hoc Retrieval." Information 13, no. 5 (2022): 221. http://dx.doi.org/10.3390/info13050221.

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Nowadays, pre-trained language models such as Bidirectional Encoder Representations from Transformer (BERT) are becoming a basic building block in Information Retrieval tasks. Nevertheless, there are several limitations when applying BERT to the query-document matching task: (1) relevance assessments are applicable at the document-level, and the tokens of documents often exceed the maximum input length of BERT; (2) applying BERT to long documents leads to a great consumption of memory usage and run time, owing to the computational cost of the interactions between tokens. This paper explores a

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Zhou, Xiaohua, Xiaohua Hu, and Xiaodan Zhang. "Topic Signature Language Models for Ad hoc Retrieval." IEEE Transactions on Knowledge and Data Engineering 19, no. 9 (2007): 1276–87. http://dx.doi.org/10.1109/tkde.2007.1058.

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Dang, Edward Kai Fung, Robert Wing Pong Luk, and James Allan. "A Comparison between Term-Independence Retrieval Models for Ad Hoc Retrieval." ACM Transactions on Information Systems 40, no. 3 (2022): 1–37. http://dx.doi.org/10.1145/3483612.

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In Information Retrieval, numerous retrieval models or document ranking functions have been developed in the quest for better retrieval effectiveness. Apart from some formal retrieval models formulated on a theoretical basis, various recent works have applied heuristic constraints to guide the derivation of document ranking functions. While many recent methods are shown to improve over established and successful models, comparison among these new methods under a common environment is often missing. To address this issue, we perform an extensive and up-to-date comparison of leading term-indepen

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Deveaud, Romain, Eric SanJuan, and Patrice Bellot. "Accurate and effective latent concept modeling for ad hoc information retrieval." Document numérique 17, no. 1 (2014): 61–84. http://dx.doi.org/10.3166/dn.17.1.61-84.

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Khodabakhsh, Maryam, and Ebrahim Bagheri. "Semantics-enabled query performance prediction for ad hoc table retrieval." Information Processing & Management 58, no. 1 (2021): 102399. http://dx.doi.org/10.1016/j.ipm.2020.102399.

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Garigliotti, Darío, Faegheh Hasibi, and Krisztian Balog. "Identifying and exploiting target entity type information for ad hoc entity retrieval." Information Retrieval Journal 22, no. 3-4 (2018): 285–323. http://dx.doi.org/10.1007/s10791-018-9346-x.

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Azzopardi, Leif. "Incorporating context within the language modeling approach for ad hoc information retrieval." ACM SIGIR Forum 40, no. 1 (2006): 70. http://dx.doi.org/10.1145/1147197.1147211.

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Su, Weihang, Qingyao Ai, Xiangsheng Li, et al. "Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-Hoc Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 19026–34. http://dx.doi.org/10.1609/aaai.v38i17.29869.

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With the development of deep learning and natural language processing techniques, pre-trained language models have been widely used to solve information retrieval (IR) problems. Benefiting from the pre-training and fine-tuning paradigm, these models achieve state-of-the-art performance. In previous works, plain texts in Wikipedia have been widely used in the pre-training stage. However, the rich structured information in Wikipedia, such as the titles, abstracts, hierarchical heading (multi-level title) structure, relationship between articles, references, hyperlink structures, and the writing

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P. Bhopale, Bhopale, and Ashish Tiwari. "LEVERAGING NEURAL NETWORK PHRASE EMBEDDING MODEL FOR QUERY REFORMULATION IN AD-HOC BIOMEDICAL INFORMATION RETRIEVAL." Malaysian Journal of Computer Science 34, no. 2 (2021): 151–70. http://dx.doi.org/10.22452/mjcs.vol34no2.2.

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This study presents a spark enhanced neural network phrase embedding model to leverage query representation for relevant biomedical literature retrieval. Information retrieval for clinical decision support demands high precision. In recent years, word embeddings have been evolved as a solution to such requirements. It represents vocabulary words in low-dimensional vectors in the context of their similar words; however, it is inadequate to deal with semantic phrases or multi-word units. Learning vector embeddings for phrases by maintaining word meanings is a challenging task. This study propose

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Lin, Sheng-Chieh, Jheng-Hong Yang, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, and Jimmy Lin. "Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting." ACM Transactions on Information Systems 39, no. 4 (2021): 1–29. http://dx.doi.org/10.1145/3446426.

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Conversational search plays a vital role in conversational information seeking. As queries in information seeking dialogues are ambiguous for traditional ad hoc information retrieval (IR) systems due to the coreference and omission resolution problems inherent in natural language dialogue, resolving these ambiguities is crucial. In this article, we tackle conversational passage retrieval, an important component of conversational search, by addressing query ambiguities with query reformulation integrated into a multi-stage ad hoc IR system. Specifically, we propose two conversational query refo

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Yuan-Po Cheng, Chia-Yi Wu, Yao-Jen Tang, and Ming-Jer Tsai. "Retrieval-Guaranteed Location-Aware Information Brokerage Scheme in 3D Wireless Ad Hoc Networks." IEEE Transactions on Computers 62, no. 4 (2013): 798–812. http://dx.doi.org/10.1109/tc.2012.16.

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Yang, Bo, and Manohar Mareboyana. "Location-Aware Caching for Semantic-Based Image Queries in Mobile AD HOC Networks." International Journal of Multimedia Data Engineering and Management 3, no. 1 (2012): 17–35. http://dx.doi.org/10.4018/jmdem.2012010102.

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Mobile image information retrieval, i.e., the processing of mobile image queries, has attracted research attention due to the recent technological advances in mobile and ubiquitous computing, network infrastructures, and multimedia streaming. The previous research focuses on data delivery, while few works have reported on content-based mobile information retrieval. Therefore, it is important to devise effective means to describe the semantics as well as content distribution of mobile data. Caching is an attractive solution that helps reveal semantic relationships among mobile data sources. How

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Ben Basat, Ran, Moshe Tennenholtz, and Oren Kurland. "A Game Theoretic Analysis of the Adversarial Retrieval Setting." Journal of Artificial Intelligence Research 60 (December 30, 2017): 1127–64. http://dx.doi.org/10.1613/jair.5547.

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The main goal of search engines is ad hoc retrieval: ranking documents in a corpus by their relevance to the information need expressed by a query. The Probability Ranking Principle (PRP) --- ranking the documents by their relevance probabilities --- is the theoretical foundation of most existing ad hoc document retrieval methods. A key observation that motivates our work is that the PRP does not account for potential post-ranking effects; specifically, changes to documents that result from a given ranking. Yet, in adversarial retrieval settings such as the Web, authors may consistently try to

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Kwok, Kui-Lam, Laszlo Grunfeld, and Peter Deng. "Employing web mining and data fusion to improve weak ad hoc retrieval." Information Processing & Management 43, no. 2 (2007): 406–19. http://dx.doi.org/10.1016/j.ipm.2006.07.008.

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Zhai, Chengxiang, and John Lafferty. "A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval." ACM SIGIR Forum 51, no. 2 (2017): 268–76. http://dx.doi.org/10.1145/3130348.3130377.

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Alowish, Mazen, Yoshiaki Shiraishi, Yasuhiro Takano, Masami Mohri, and Masakatu Morii. "A novel software-defined networking controlled vehicular named-data networking for trustworthy emergency data dissemination and content retrieval assisted by evolved interest packet." International Journal of Distributed Sensor Networks 16, no. 3 (2020): 155014772090928. http://dx.doi.org/10.1177/1550147720909280.

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Vehicle ad hoc network is the key technology for a future Internet of vehicles and intelligent transport system. However, involvement of vast number of vehicles in Internet of vehicles limits the performance of vehicle ad hoc network. To tackle this problem, a novel vehicle ad hoc network architecture with two different technologies such as software-defined networking and named-data networking is proposed in this article. In the proposed software-defined networking controlled vehicular named-data networking, IP addressing issue is resolved by named-data networking and global view of the networ

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MYLONAS, PH, D. VALLET, P. CASTELLS, M. FERNÁNDEZ, and Y. AVRITHIS. "Personalized information retrieval based on context and ontological knowledge." Knowledge Engineering Review 23, no. 1 (2008): 73–100. http://dx.doi.org/10.1017/s0269888907001282.

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AbstractContext modeling has long been acknowledged as a key aspect in a wide variety of problem domains. In this paper we focus on the combination of contextualization and personalization methods to improve the performance of personalized information retrieval. The key aspects in our proposed approach are (1) the explicit distinction between historic user context and live user context, (2) the use of ontology-driven representations of the domain of discourse, as a common, enriched representational ground for content meaning, user interests, and contextual conditions, enabling the definition o

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MÜLLER, CHRISTOF, IRYNA GUREVYCH, and MAX MÜHLHÄUSER. "CLOSING THE VOCABULARY GAP FOR COMPUTING TEXT SIMILARITY AND INFORMATION RETRIEVAL." International Journal of Semantic Computing 02, no. 02 (2008): 253–72. http://dx.doi.org/10.1142/s1793351x08000452.

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This paper studies the integration of lexical semantic knowledge in two related semantic computing tasks: ad-hoc information retrieval and computing text similarity. For this purpose, we compare the performance of two algorithms: (i) using semantic relatedness, and (ii) using a conventional extended Boolean model [13] with additional query expansion. For the evaluation, we use two different test collections in the German language especially suitable to study the vocabulary gap problem: (i) GIRT [5] for the information retrieval task, and (ii) a collection of descriptions of professions built t

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Zhang, Peng, Wenjie Hui, Benyou Wang, et al. "Complex-valued Neural Network-based Quantum Language Models." ACM Transactions on Information Systems 40, no. 4 (2022): 1–31. http://dx.doi.org/10.1145/3505138.

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Language modeling is essential in Natural Language Processing and Information Retrieval related tasks. After the statistical language models, Quantum Language Model (QLM) has been proposed to unify both single words and compound terms in the same probability space without extending term space exponentially. Although QLM achieved good performance in ad hoc retrieval, it still has two major limitations: (1) QLM cannot make use of supervised information, mainly due to the iterative and non-differentiable estimation of the density matrix, which represents both queries and documents in QLM. (2) QLM

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Goeuriot, Lorraine, Gareth J. F. Jones, Liadh Kelly, et al. "An analysis of evaluation campaigns in ad-hoc medical information retrieval: CLEF eHealth 2013 and 2014." Information Retrieval Journal 21, no. 6 (2018): 507–40. http://dx.doi.org/10.1007/s10791-018-9331-4.

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T, Gurumekala, Indira Gandhi S, and Senthil Sivakumar M. "122 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number: A10301291S52019/2019©BEIESP DOI:10.35940/ijeat.A1030.1291S519 Journal Website: www.ijeat.org Routing Protocols for AANET." International Journal of Engineering and Advanced Technology 9, no. 1s5 (2019): 122–26. http://dx.doi.org/10.35940/ijeat.a1030.1291s519.

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Ad hoc network is an infrastructure less, self-configuring and dynamic network where the nodes are able to forward the information to other nodes based on connectivity and routing algorithm they follow. Recently, the concept of this ad hoc networking has been introduced among aircrafts for providing in-flight communication and to manage increased flow of data produced by civil aviation. The aircraft communication can be established either by satellites or cellular based systems. Utilizing satellites for the communication is very expensive and prone to high propagation delay. The cellular based

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Jermey, Jonathan. "Locating files on computer disks." Indexer: The International Journal of Indexing 22, no. 3 (2001): 130–32. http://dx.doi.org/10.3828/indexer.2001.22.3.7.

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Over the last 20 years file storage capacity on personal computer systems has grown from the equivalent of a desk drawer to that of a medium-sized public library, and the trend is continuing. File retrieval methods have been developed on an ad hoc basis which has generally failed to keep up with this growth, but there are some encouraging and interesting developments in this area.

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Faggioli, Guglielmo, Nicola Ferro, Josiane Mothe, Fiana Raiber, and Maik Fröbe. "Report on the 1st Workshop on Query Performance Prediction and Its Evaluation in New Tasks (QPP++ 2023) at ECIR 2023." ACM SIGIR Forum 57, no. 1 (2023): 1–7. http://dx.doi.org/10.1145/3636341.3636356.

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Query Performance Prediction (QPP) is currently primarily applied to ad-hoc retrieval tasks. The Information Retrieval (IR) field is reaching new heights thanks to recent advances in large language models and neural networks, as well as emerging new ways of searching, such as conversational search. Such advancements are quickly spreading to adjacent research areas, including QPP, necessitating a reconsideration of how we perform and evaluate QPP. This workshop sought to elicit discussion on three topics related to the future of QPP: exploiting advances in IR to improve QPP, instantiating QPP o

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Valcarce, Daniel. "Information retrieval models for recommender systems." ACM SIGIR Forum 53, no. 1 (2019): 44–45. http://dx.doi.org/10.1145/3458537.3458545.

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Information retrieval addresses the information needs of users by delivering relevant pieces of information but requires users to convey their information needs explicitly. In contrast, recommender systems offer personalized suggestions of items automatically. Ultimately, both fields help users cope with information overload by providing them with relevant items of information. This thesis aims to explore the connections between information retrieval and recommender systems. Our objective is to devise recommendation models inspired in information retrieval techniques. We begin by borrowing ide

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Ai, Qingyao. "Neural generative models and representation learning for information retrieval." ACM SIGIR Forum 53, no. 2 (2019): 97. http://dx.doi.org/10.1145/3458553.3458565.

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Information Retrieval (IR) concerns about the structure, analysis, organization, storage, and retrieval of information. Among different retrieval models proposed in the past decades, generative retrieval models, especially those under the statistical probabilistic framework, are one of the most popular techniques that have been widely applied to Information Retrieval problems. While they are famous for their well-grounded theory and good empirical performance in text retrieval, their applications in IR are often limited by their complexity and low extendability in the modeling of high-dimensio

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Arguello, Jaime, Jonathan L. Elsas, Jamie Callan, and Jaime Carbonell. "Document Representation and Query Expansion Models for Blog Recommendation." Proceedings of the International AAAI Conference on Web and Social Media 2, no. 1 (2021): 10–18. http://dx.doi.org/10.1609/icwsm.v2i1.18605.

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We explore several different document representation models and two query expansion models for the task of recommending blogs to a user in response to a query. Blog relevance ranking differs from traditional document ranking in ad-hocinformation retrieval in several ways: (1) the unit of output (the blog) is composed of a collection of documents (the blog posts) rather than a single document, (2) the query represents an ongoing and typically multifaceted interest in the topic rather than a passing ad-hoc information need and (3) due to the propensity of spam, splogs, and tangential comments, t

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MARAGOUDAKIS, MANOLIS, DIMITRIOS P. LYRAS, and KYRIAKOS SGARBAS. "BAYESIAN RETRIEVAL USING A SIMILARITY-BASED LEMMATIZER." International Journal on Artificial Intelligence Tools 21, no. 05 (2012): 1250024. http://dx.doi.org/10.1142/s0218213012500248.

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The present paper describes a Bayesian network approach to Information Retrieval (IR) from Web documents. The network structure provides an intuitive representation of uncertainty relationships and the embedded conditional probability table is used by inference algorithms in an attempt to identify documents that are relevant to the user's needs, expressed in the form of Boolean queries. Our research has been directed in constructing a probabilistic IR framework that focus on assisting users to perform Ad-hoc retrieval of documents from the various domains such as economics, news, sports, etc.

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Arnold, Jeffrey L., Brian Neil Levine, R. Manmatha, et al. "Information-Sharing in Out-of-Hospital Disaster Response: The Future Role of Information Technology." Prehospital and Disaster Medicine 19, no. 3 (2004): 201–7. http://dx.doi.org/10.1017/s1049023x00001783.

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AbstractNumerous examples exist of the benefits of the timely access to information in emergencies and disasters. Information technology (IT) is playing an increasingly important role in information-sharing during emergencies and disasters.The effective use of IT in out-of-hospital (OOH) disaster response is accompanied by numerous challenges at the human, applications, communication, and security levels.Most reports of IT applications to emergencies or disasters to date, concern applications that are hospital-based or occur during non-response phases of events (i.e., mitigation, planning and

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Klaeren, H., and F. Banhart. "A Graphical Query Generator for Clinical Research Databases." Methods of Information in Medicine 34, no. 04 (1995): 328–39. http://dx.doi.org/10.1055/s-0038-1634607.

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Abstract:Clinical research involves recording, storage and retrieval of disease-related patient data, typically using a database system. In order to facilitate ad hoc queries to clinical databases we have developed a query generator with a graphical interface. The query generator uses an object-oriented data model which is visualized by directed graphs. The main focus of our work was the definition of object-oriented user views to the partly complex data structures of a relational database. Furthermore, we tried to define graphical abstractions for all common types of queries. Thus, even for n

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Devezas, José. "Graph-based entity-oriented search." ACM SIGIR Forum 55, no. 1 (2021): 1–2. http://dx.doi.org/10.1145/3476415.3476430.

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Entity-oriented search has revolutionized search engines. In the era of Google Knowledge Graph and Microsoft Satori, users demand an effortless process of search. Whether they express an information need through a keyword query, expecting documents and entities, or through a clicked entity, expecting related entities, there is an inherent need for the combination of corpora and knowledge bases to obtain an answer. Such integration frequently relies on independent signals extracted from inverted indexes, and from quad indexes indirectly accessed through queries to a triplestore. However, relyin

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MacAvaney, Sean. "Effective and practical neural ranking." ACM SIGIR Forum 55, no. 1 (2021): 1–2. http://dx.doi.org/10.1145/3476415.3476432.

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Supervised machine learning methods that use neural networks ("deep learning") have yielded substantial improvements to a multitude of Natural Language Processing (NLP) tasks in the past decade. Improvements to Information Retrieval (IR) tasks, such as ad-hoc search, lagged behind those in similar NLP tasks, despite considerable community efforts. Although there are several contributing factors, I argue in this dissertation that early attempts were not more successful because they did not properly consider the unique characteristics of IR tasks when designing and training ranking models. I fir

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Achemoukh, Farida, and Rachid Ahmed-Ouamer. "Integration of User Profile in Search Process according to the Bayesian Approach." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 6, no. 4 (2018): 32. http://dx.doi.org/10.3991/ijes.v6i4.9716.

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Most information retrieval system (IRS) rely on the so called system-centred approach,<br /> behaves as a black box, which produces the same answer to the same query, independently on the<br /> user’s specific information needs. Without considering the user, it is hard to know which sense<br /> refers to in a query. To satisfy user needs, personalization is an appropriate solution to improve the<br /> IRS usability. Modeling the user profile can be the first step towards personalization of information<br /> search. The user profile refers to his/her interests buil

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Yeshambel, Tilahun, Josiane Mothe, and Yaregal Assabie. "Learned Text Representation for Amharic Information Retrieval and Natural Language Processing." Information 14, no. 3 (2023): 195. http://dx.doi.org/10.3390/info14030195.

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Over the past few years, word embeddings and bidirectional encoder representations from transformers (BERT) models have brought better solutions to learning text representations for natural language processing (NLP) and other tasks. Many NLP applications rely on pre-trained text representations, leading to the development of a number of neural network language models for various languages. However, this is not the case for Amharic, which is known to be a morphologically complex and under-resourced language. Usable pre-trained models for automatic Amharic text processing are not available. This

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Zhang, Xinyu, Nandan Thakur, Odunayo Ogundepo, et al. "MIRACL: A Multilingual Retrieval Dataset Covering 18 Diverse Languages." Transactions of the Association for Computational Linguistics 11 (2023): 1114–31. http://dx.doi.org/10.1162/tacl_a_00595.

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Abstract MIRACL is a multilingual dataset for ad hoc retrieval across 18 languages that collectively encompass over three billion native speakers around the world. This resource is designed to support monolingual retrieval tasks, where the queries and the corpora are in the same language. In total, we have gathered over 726k high-quality relevance judgments for 78k queries over Wikipedia in these languages, where all annotations have been performed by native speakers hired by our team. MIRACL covers languages that are both typologically close as well as distant from 10 language families and 13

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Shukla, Abhishek Kumar, Sujoy Das, Pushpendra Kumar, and Afroj Alam. "Relevance Feedback and Deep Neural Network-Based Semantic Method for Query Expansion." Wireless Communications and Mobile Computing 2022 (July 18, 2022): 1–11. http://dx.doi.org/10.1155/2022/6789044.

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Machine learning techniques have been widely used in almost every area of arts, science, and technology for the last two decades. Document analysis and query expansion also use machine learning techniques at a broad scale for information retrieval tasks. The state-of-the-art models like the Bo1 model, Bo2 model, KL divergence model, and chi-square model are probabilistic, and they work on DFR-based retrieval models. These models are much focused on term frequency and do not care about the semantic relationship among the terms. The proposed model applies the semantic method to find the semantic

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Tagliolato Acquaviva d’Aragona, Paolo, Gloria Bordogna, Lorenza Babbini, et al. "Testing Pretrained Large Language Models to Set Up a Knowledge Hub of Heterogeneous Multisource Environmental Documents." Applied Sciences 15, no. 10 (2025): 5415. https://doi.org/10.3390/app15105415.

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This contribution outlines the design of a Knowledge Hub of heterogeneous documents related to the UNEP/MAP Barcelona Convention system. The Knowledge Hub is intended to serve as a resource to assist public authorities and users with different backgrounds and needs in accessing information efficiently; users should be able to either formulate natural language queries or to navigate a knowledge graph that is automatically generated to find relevant documents. The ad hoc retrieval task and the Knowledge Hub creation are defined based on state-of-the-art Large Language Models (LLMs). Specifically

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Singh, Jagendra, and Rakesh Kumar. "Lexical Co-Occurrence and Contextual Window-Based Approach with Semantic Similarity for Query Expansion." International Journal of Intelligent Information Technologies 13, no. 3 (2017): 57–78. http://dx.doi.org/10.4018/ijiit.2017070104.

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Query expansion (QE) is an efficient method for enhancing the efficiency of information retrieval system. In this work, we try to capture the limitations of pseudo-feedback based QE approach and propose a hybrid approach for enhancing the efficiency of feedback based QE by combining corpus-based, contextual based information of query terms, and semantic based knowledge of query terms. First of all, this paper explores the use of different corpus-based lexical co-occurrence approaches to select an optimal combination of query terms from a pool of terms obtained using pseudo-feedback based QE. N

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Rao, Jinfeng, Wei Yang, Yuhao Zhang, Ferhan Ture, and Jimmy Lin. "Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 232–40. http://dx.doi.org/10.1609/aaai.v33i01.3301232.

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Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have mostly been applied to “standard” ad hoc retrieval tasks over web pages and newswire articles. This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network), a novel neural ranking model specifically designed for ranking short social media posts. We identify document length, informal language, and heterogeneous relevance signals as features that distinguish documents in our domain, and present a model specifically designed with these characteristics i

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Gall, W., G. Duftschmid, and W. Dorda. "Clinical Data Retrieval: 25 Years of Temporal Query Management at the University of Vienna Medical School." Methods of Information in Medicine 41, no. 02 (2002): 89–97. http://dx.doi.org/10.1055/s-0038-1634291.

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Summary Objectives: Today, many clinical information systems include analysis components which allow clinicians to apply a selection of predefined statistical functions that satisfy typical cases. They are mostly to inflexible to handle complex, non-standard problems, however. The focus of this paper, therefore, is to present an approach that enables clinicians to autonomously create ad hoc queries including temporal relations in an interactive environment. Methods: We developed the query language AMAS, which was specifically customized for users from the medical domain to flexibly retrieve an

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Deris, A., I. Trigonis, A. Aravanis, and E. K. Stathopoulou. "DEPTH CAMERAS ON UAVs: A FIRST APPROACH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W3 (February 23, 2017): 231–36. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w3-231-2017.

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Accurate depth information retrieval of a scene is a field under investigation in the research areas of photogrammetry, computer vision and robotics. Various technologies, active, as well as passive, are used to serve this purpose such as laser scanning, photogrammetry and depth sensors, with the latter being a promising innovative approach for fast and accurate 3D object reconstruction using a broad variety of measuring principles including stereo vision, infrared light or laser beams. In this study we investigate the use of the newly designed Stereolab's ZED depth camera based on passive ste

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Järvelin, Kalervo, Peter Ingwersen, and Timo Niemi. "A user‐oriented interface for generalised informetric analysis based on applying advanced data modelling techniques." Journal of Documentation 56, no. 3 (2000): 250–78. http://dx.doi.org/10.1108/eum0000000007115.

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This article presents a novel user‐oriented interface for generalised informetric analysis and demonstrates how informetric calculations can easily and declaratively be specified through advanced data modelling techniques. The interface is declarative and at a high level. Therefore it is easy to use, flexible and extensible. It enables end users to perform basic informetric ad hoc calculations easily and often with much less effort than in contemporary online retrieval systems. It also provides several fruitful generalisations of typical informetric measurements like impact factors. These are

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Pinto, Jaime A., and Mauricio B. Almeida. "An applied ontology-Oriented Case Study to Distinguish Public and Private Institutions Through Their Documents." KNOWLEDGE ORGANIZATION 47, no. 7 (2020): 582–91. http://dx.doi.org/10.5771/0943-7444-2020-7-582.

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The institutions we create shape many of the activities we engage insofar as they are pervasive entities in our society. In an era full of new technologies, including the semantic web, there is a movement toward sound conceptual modeling for socio-technical solutions applied to government institutions. To develop these complex solutions, one needs to deepen the ontological status of entities in the institutional domain, because literature is full of ambiguous and ad-hoc hypotheses about distinctions between public and private corporations. We believe we can find better explanations for such di

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Álvarez-Rodríguez, Jose María, Ricardo Colomo-Palacios, and Vladimir Stantchev. "Skillrank: Towards a Hybrid Method to Assess Quality and Confidence of Professional Skills in Social Networks." Scientific Programming 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/451476.

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The present paper introduces a hybrid technique to measure the expertise of users by analyzing their profiles and activities in social networks. Currently, both job seekers and talent hunters are looking for new and innovative techniques to filter jobs and candidates where candidates are trying to improve and make their profiles more attractive. In this sense, the Skillrank approach is based on the conjunction of existing and well-known information and expertise retrieval techniques that perfectly fit the existing web and social media environment to deliver an intelligent component to integrat

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De La Iglesia, B., S. Donell, V. Rayward-Smith, and J. Bettencourt-Silva. "On Creating a Patient-centric Database from Multiple Hospital Information Systems." Methods of Information in Medicine 51, no. 03 (2012): 210–20. http://dx.doi.org/10.3414/me10-01-0069.

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SummaryBackground: The information present in Hospital Information Systems (HIS) is heterogeneous and is used primarily by health practitioners to support and improve patient care. Conducting clinical research, data analyses or knowledge discovery projects using electronic patient data in secondary care centres relies on accurate data collection, which is often an ad-hoc process poorly described in the literature.Objectives: This paper aims at facilitating and expanding on the process of retrieving and collating patient-centric data from multiple HIS for the purpose of creating a research data

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Saleem, Qudsia, Ikram Ud Din, Ahmad Almogren, Ibrahim Alkhalifa, Hasan Ali Khattak, and Joel J. P. C. Rodrigues. "Named Data Networking-Based On-Demand Secure Vehicle-To-Vehicle Communications." Wireless Communications and Mobile Computing 2021 (November 26, 2021): 1–15. http://dx.doi.org/10.1155/2021/1615015.

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The detection of secure vehicles for content placement in vehicle to vehicle (V2V) communications makes a challenging situation for a well-organized dynamic nature of vehicular ad hoc networks (VANET). With the increase in the demand of efficient and adoptable content delivery, information-centric networking (ICN) can be a promising solution for the future needs of the network. ICN provides a direct retrieval of content through its unique name, which is independent of locations. It also performs better in content retrieval with its in-network caching and named-based routing capabilities. Since

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Jiang, Nanlan, Sai Yang, and Pingping Xu. "Enabling Location Privacy Preservation in MANETs Based on Distance, Angle, and Spatial Cloaking." Electronics 9, no. 3 (2020): 458. http://dx.doi.org/10.3390/electronics9030458.

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Preserving the location privacy of users in Mobile Ad hoc Networks (MANETs) is a significant challenge for location information. Most of the conventional Location Privacy Preservation (LPP) methods protect the privacy of the user while sacrificing the capability of retrieval on the server-side, that is, legitimate devices except the user itself cannot retrieve the location in most cases. On the other hand, applications such as geographic routing and location verification require the retrievability of locations on the access point, the base station, or a trusted server. Besides, with the develo

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