Retrieval Language Research Articles

The current trends in the development of methods and regulatory framework for indexing library information resources

The author examines the development of the basic concepts of indexing information resources using classifications and dictionaries of information retrieval languages. The impact of digital communications and the Internet on the indexing methods in the information environment, including the libraries, is emphasized. The modern library collections comprise various types of resources, both traditional paper publications and digital resources of various types, widely available on the Internet. In these conditions, harmonization of indexing methods with the principles of Internet information retrieval grows in importance. The author discusses the regulatory framework for indexing in the library sector and its relationship with Internet standards within the concept of Semantic Web. The author reviews the main stages of the development of the regulatory framework both on the international level and in this country. The author emphasizes the need to study the experience of other organizations in the field of standardization for further development of indexing technology.

Enhancing multilingual information retrieval: The efficacy of hybrid approaches

Multilingual Information Retrieval (MLIR) plays a crucial role in accessing information across different languages. This paper explores various techniques and tools used in Cross-Language Information Retrieval (CLIR), focusing on query translation, document translation, and hybrid approaches. Query translation employs bilingual dictionaries and machine translation systems to convert user queries from one language to another, whereas document translation involves translating documents into the query language for indexing and retrieval. Hybrid approaches combine these methods to optimize retrieval performance, leveraging the strengths of both to address their individual limitations. Our comparative analysis shows that hybrid systems consistently outperform standalone query or document translation systems, achieving higher precision, recall, and user satisfaction. For instance, hybrid systems in multilingual legal document retrieval tasks achieved precision rates of 88%, recall rates of 82%, and an F1 score of 0.85. These results underscore the effectiveness of hybrid approaches in handling the complexities of MLIR, providing more accurate and comprehensive retrieval outcomes. This study highlights the practical benefits of hybrid CLIR systems and suggests directions for future research in enhancing multilingual access to information.

Towards Fast and Accurate Image-Text Retrieval With Self-Supervised Fine-Grained Alignment

Image-text retrieval requires the system to bridge the heterogenous gap between vision and language for accurate retrieval while keeping the network lightweight-enough for efficient retrieval. Existing trade-off solutions mainly study from the view of incorporating cross-modal interactions with the independent-embedding framework or leveraging stronger pre-trained encoders, which still demand time-consuming similarity measurement or heavyweight model structure in the retrieval stage. In this work, we propose an image-text alignment module SelfAlign on top of the independent-embedding framework, which improves the retrieval accuracy while maintains the retrieval efficiency without extra supervision. SelfAlign contains two collaborative sub-modules that force image-text alignment at both the concept level and context level by self-supervised contrastive learning. It doesn't require cross-modal embedding interactions during training while maintaining independent image and text encoders during retrieval. With comparable time cost, SelfAlign consistently boosts the accuracy of state-of-the-art non-pre-training independent-embedding models respectively by 9.1%, 4.2%, and 6.6% in terms of R@sum score on Flickr30K, MS-COCO 1K and MS-COCO 5K datasets. The retrieval accuracy also outperforms most existing interactive-embedding models with orders of magnitude decrease in retrieval time. The source code is available at: https://github.com/Zjamie813/SelfAlign.

Multiple sclerosis is associated with differences in semantic memory structure.

Although language is often considered to be largely intact in multiple sclerosis (MS), word-finding difficulties are a common complaint. Recent work suggests that declines in language are not solely the result of motoric and cognitive slowing that is most strongly associated with MS. Network science approaches have been effectively used to examine network structure as it relates to clinical conditions, aging, and language. The present study utilizes a network science approach to investigate whether individuals with MS exhibit less interconnected and resilient semantic networks compared to age-matched neurotypical peers. We used semantic fluency data from 89 participants with MS and 88 neurotypical participants to estimate and analyze the semantic network structure for each participant group. Additionally, we conducted a percolation analysis to examine the resilience of each network. Network measures showed that individuals with MS had lower local and global clustering coefficients, longer average shortest path lengths, and higher modularity values compared to neurotypical peers. Small-worldness, network portrait divergence measures, and community detection analyses were consistent with these results and indicated that macroscopic properties of the two networks differed and that the semantic network for individuals with MS was more fractured than the neurotypical peer network. Moreover, a spreading activation simulation and percolation analysis suggested that the semantic networks of individuals with MS are less flexible and activation degrades faster than those of age-matched neurotypical participants. These differing semantic network structures suggest that language retrieval difficulties in MS partially result from decline in language-specific factors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Retracted: English Audio Language Retrieval Based on Adaptive Speech-Adjusting Algorithm

Retracted: English Audio Language Retrieval Based on Adaptive Speech-Adjusting Algorithm

Developing linguistic tools of thematic search in library information systems

Within the R&D program “Information support of research by scientists and specialists on the basis of RNPLS&T Open Archive – the system of scientific knowledge aggregation”, the RNPLS&T analyzes the use of linguistic tools of thematic search in the modern library information systems and the prospects for their development. The author defines the key common characteristics of e-catalogs of the largest Russian libraries revealed at the first stage of the analysis. Based on the specified common characteristics and detailed comparison analysis, the author outlines and substantiates the vectors for enhancing search inter faces of e-catalogs. The focus is made on linguistic tools of thematic search in library information systems; the key vectors are suggested: use of thematic search at different search levels with the clear-cut level differentiation; use of combined functionality within thematic search system; implementation of classification search in all e-catalogs; hierarchical representation of classifications; use of the matching systems for classification information retrieval languages, and in the long term classification and verbal information retrieval languages, and various verbal information retrieval languages. The author formulates practical recommendations to improve thematic search in library information systems.

Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications

Multimodality Representation Learning, as a technique of learning to embed information from different modalities and their correlations, has achieved remarkable success on a variety of applications, such as Visual Question Answering (VQA), Natural Language for Visual Reasoning (NLVR), and Vision Language Retrieval (VLR). Among these applications, cross-modal interaction and complementary information from different modalities are crucial for advanced models to perform any multimodal task, e.g., understand, recognize, retrieve, or generate optimally. Researchers have proposed diverse methods to address these tasks. The different variants of transformer-based architectures performed extraordinarily on multiple modalities. This survey presents the comprehensive literature on the evolution and enhancement of deep learning multimodal architectures to deal with textual, visual and audio features for diverse cross-modal and modern multimodal tasks. This study summarizes the ( i ) recent task-specific deep learning methodologies, ( ii ) the pretraining types and multimodal pretraining objectives, ( iii ) from state-of-the-art pretrained multimodal approaches to unifying architectures, and ( iv ) multimodal task categories and possible future improvements that can be devised for better multimodal learning. Moreover, we prepare a dataset section for new researchers that covers most of the benchmarks for pretraining and finetuning. Finally, major challenges, gaps, and potential research topics are explored. A constantly-updated paperlist related to our survey is maintained at https://github.com/marslanm/multimodality-representation-learning .

CLASSIM: A relational database driven crop model interface

Crop models are valuable tools for examining the interactions of cultivar characteristics, environment, and management practices, and how they affect crop growth and development. The difficulty in finding all the data needed to set up a simulation can often deter potential users from utilizing a crop model. Model interfaces are necessary to make these complex tools accessible to end-users who may lack the expertise needed to work with the models directly, but who would benefit from the information generated by the models. As crop models vary in terms of input and output structures, there is no one universally compatible interface, so different crop model suites require their own interface. CLASSIM is a graphical user interface (GUI) for a suite of models developed by USDA-ARS. The interface provides a simplified tool to assemble input data for a model simulation. Web access to online databases for soil and weather data simplifies finding site-related data. The user is guided step by step to build the necessary input files. A database structure facilitates data entry, storage, and retrieval. The use of Structured Query Language (SQL) for data retrieval allows for advanced analysis and visualization outside of the interface. We describe the features of the GUI and provide examples of various operations. Model outputs are provided in tables and graphs to allow for visualization and analysis. Two-dimensional contour plots of soil processes are provided to visualize output from the two-dimensional finite element soil model. CLASSIM also allows users to set up single- and multiple-season runs for maize, cotton, soybean, potato, and fallow treatments, and is expected to be expanded to more crops in the future. A user-friendly GUI is essential for making simulation tools accessible to the broad range of researchers and practitioners working to solve the agricultural system challenges.

Glioblastoma remodelling of human neural circuits decreases survival

Gliomas synaptically integrate into neural circuits1,2. Previous research has demonstrated bidirectional interactions between neurons and glioma cells, with neuronal activity driving glioma growth1–4 and gliomas increasing neuronal excitability2,5–8. Here we sought to determine how glioma-induced neuronal changes influence neural circuits underlying cognition and whether these interactions influence patient survival. Using intracranial brain recordings during lexical retrieval language tasks in awake humans together with site-specific tumour tissue biopsies and cell biology experiments, we find that gliomas remodel functional neural circuitry such that task-relevant neural responses activate tumour-infiltrated cortex well beyond the cortical regions that are normally recruited in the healthy brain. Site-directed biopsies from regions within the tumour that exhibit high functional connectivity between the tumour and the rest of the brain are enriched for a glioblastoma subpopulation that exhibits a distinct synaptogenic and neuronotrophic phenotype. Tumour cells from functionally connected regions secrete the synaptogenic factor thrombospondin-1, which contributes to the differential neuron–glioma interactions observed in functionally connected tumour regions compared with tumour regions with less functional connectivity. Pharmacological inhibition of thrombospondin-1 using the FDA-approved drug gabapentin decreases glioblastoma proliferation. The degree of functional connectivity between glioblastoma and the normal brain negatively affects both patient survival and performance in language tasks. These data demonstrate that high-grade gliomas functionally remodel neural circuits in the human brain, which both promotes tumour progression and impairs cognition.

The cost of changing language context: The language-dependent recall of fictional stories.

Language-dependent recall refers to the language-specific retrieval of memories in which the retrieval success depends on the match between the languages of encoding and retrieval. The present study investigated language-dependent recall in terms of memory accuracy, false memory, and episodic memory characteristics in the free recall of fictional stories. We also asked how language-dependent memories were influenced by language proficiency and visual imagery. One hundred and thirty-seven native Turkish (L1) speakers who were second-language learners of English (L2) were divided into four groups in which they read fictional stories and then recalled them: (1) Turkish reading-Turkish recall, (2) English reading-English recall, (3) English reading-Turkish recall, (4) Turkish reading-English recall. Regardless of the match between L1 or L2, accuracy was higher when participants read and recalled the stories in the same language than when they did it in different languages, showing the language-dependent recall effect. Notably, the effect of match or mismatch between encoding and retrieval languages on accuracy did not depend on L2 proficiency and visual imagery. In addition, false memories were salient, particularly for participants who read the stories in L2 but retrieved them in L1. Overall, our findings suggest that accuracy-oriented memory research provides a comprehensive investigation of language-dependent recall, addressing the links of language-dependent memories with accuracy, false memory, and episodic memory characteristics.

Медиаэкология в структуре библиотечно-информационных программ

The genesis of media ecology as an independent scientific, practical and pedagogical direction of inquiry is explored. Its interdisciplinary essence is characterized and cross-curriculum connections are established with the disciplines of the library and information cycle, primarily with bibliology and book history. Two vectors of research in the field of media ecology are identified: technocentric and anthropocentric, and their fundamental methodological foundations are outlined in the context of a network understanding of the architecture of library and information activities. Based on the analysis of scientific literature and legal documents regulating library and information education, the key pedagogical tasks of media ecology are formulated: developing skills in identifying unreliable information and techniques of media influence, organization of professional activities in conditions of information overload, training in data verification technologies, bibliographic heuristics and professional navigation in specialized resources, dissemination of knowledge about models of text-mediated communications, information retrieval languages and the principles of their functioning.

DCRL-KG: Distributed Multi-Modal Knowledge Graph Retrieval Platform Based on Collaborative Representation Learning

The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval platforms. Image and text descriptions added to the knowledge graph enrich the node information, which accounts for the advantage of the multi-modal knowledge graph. In the field of cross-modal retrieval platforms, multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational information provided by knowledge graphs. The representation learning method is significant to the application of multi-modal knowledge graphs. This paper proposes a distributed collaborative vector retrieval platform (DCRL-KG) using the multimodal knowledge graph VisualSem as the foundation to achieve efficient and high-precision multimodal data retrieval. Firstly, use distributed technology to classify and store the data in the knowledge graph to improve retrieval efficiency. Secondly, this paper uses BabelNet to expand the knowledge graph through multiple filtering processes and increase the diversification of information. Finally, this paper builds a variety of retrieval models to achieve the fusion of retrieval results through linear combination methods to achieve high-precision language retrieval and image retrieval. The paper uses sentence retrieval and image retrieval experiments to prove that the platform can optimize the storage structure of the multi-modal knowledge graph and have good performance in multi-modal space.

Effectiveness and safety of different academic schools of traditional Chinese medicine in the treatment of obesity: A protocol for systematic review and meta-analysis.

Obesity is a global epidemic. Since 1975, the global obesity rate has almost tripled. Although many systematic reviews and clinical trials have shown that traditional Chinese medicine (TCM) can effectively treat obesity, the effectiveness and safety of different academic schools of TCM in treating obesity have not been systematically evaluated. The retrieval language of this study was Chinese and English. From the date of creation of the following data to June 2023, the data of Medline, PubMed, Embase, Cochrane Science Network, China Biomedical Literature Database, Central Controlled Trial Registration Center, and China Science Journal Database were retrieved, respectively. This study included clinical randomized controlled trials related to the treatment of obesity by different academic schools of TCM. The main outcome measures were body mass index, waist circumference, hip circumference, waist hip ratio, body fat content, fasting blood glucose, glycosylated hemoglobin, and blood lipid level. In addition, we manually searched other resources, including reference lists of identified publications, conference articles, and gray literature. This study will provide a more diverse choice of treatment options. The purpose of this study is to summarize and evaluate the effectiveness and safety of different academic schools of TCM in improving and treating obese patients from clinical trials, so as to provide more options for obesity treatment.

Information retrieval linguistic instruments in e-catalogs of Russian large libraries

The article is based on the findings of the study of linguistic instruments of information search used in e-catalogs of Russian largest libraries. For the study, the libraries with the collections of over 4 million entries were sele cted. The authors analyze search types applicable to e-catalog search interface. They specify the following search types applied in the above libraries: single-line (simple) search, standard (basic), advanced and professional search. The advanced search options are discussed, namely refinement systems, ranking and sorting of search results; at least one of these options is used in the e-catalogs under study. The authors also analyze the systems of search dictionaries, guidelines and use characteristics. The popular information retrieval languages are discussed with the conclusion made that the e-catalogs under study offer search options for various sets of bibliographic elements, among them author, title, year of publication, and subject search based on verbal languages (every library under study) and classification languages (majority of the libraries). The authors formulate the common features inherent in the libraries' e-catalogs and information retrieval linguistic instruments. They also offer recommendations on how to develop and efficiently use these instruments for upgrading library e-catalogs.

Simulation of English Word Order Sorting Based on Semionline Model and Artificial Intelligence.

To improve the word order ranking effect of English language retrieval, based on machine learning algorithms, this paper combines a semionline model to construct an artificial intelligence ranking model for English word order based on a semionline model and establishes a semisupervised ELM regression model. Moreover, this paper derives the mathematical model of semisupervised ELM in detail and uses FCM clustering to screen credible samples, ELM collaborative training to mark each other's samples, and the marked samples to calculate the output weights of semisupervised ELM regression. In addition, based on continuous learning of OSELMR, this paper uses confidence evaluation to screen out credible unlabeled samples, OSELM collaborative training to mark the credible samples with each other, and credible unlabeled samples to calculate the output weight of SSOSELMR. Finally, this paper designs a control experiment to analyze the model algorithm, compares and counts the parameters, and draws a statistical graph. The research results show that the model constructed in this paper is effective.

USING GESTURES: INTENTIONAL TEACHING GESTURES AS AN L2 FACILITATIVE TOOL

Gestures used for classroom pedagogical purposes, or Intentional Teaching Gestures (ITG), are often used in second language (L2) programs, however, little is known of their impact on L2 learning. This study of students’ use of ITG was carried out to address the impact of ITG on L2 learning in a primary school context. The research approach combined a naturalistic inquiry in a case study school with mixed methods to investigate students’ use of ITG and patterns of use. This article examines the data relating to students’ use of ITG in two oral language tasks. Findings reveal that more students used ITG when orally telling a narrative that had been introduced to them with ITG and when viewing ITG. Students used ITG to demonstrate meaning and to support language retrieval and ITG use often preceded oral production. The findings highlight the cognitive role of ITG in language recall and as the primary mode of communicating meaning when there is a gesture-word mismatch. Learners’ use of ITG provide insights into their stage of language development and their language gaps and are useful as a diagnostic tool for teachers. The findings contribute practical understandings towards language teaching pedagogy and practice.

Automatic Classification of Basic Nursing Teaching Resources Based on the Fusion of Multiple Neural Networks

Automatic classification is one of the hot topics in the field of information retrieval and natural language processing, but it still faces many problems to be solved. The classic automated classification approach has a sluggish classification speed and poor processing accuracy for resources with a large quantity of data. Based on this, an automated classification approach based on the integration of various neural networks for fundamental nursing teaching materials was presented. The automatic classification method of teaching resources was designed by extracting the characteristics of teaching resources, establishing the model of multiple neural network integration, and designing the classification index of basic nursing teaching resources. The experimental findings suggest that this technique has higher chi-square test parameters and better outcomes for the automated classification of large instructional materials than the classic rough set automatic classification method.

Adenosine triphosphate-binding cassette subfamily C members in liver hepatocellular carcinoma: Bioinformatics-driven prognostic value.

Aberrant expression of adenosine triphosphate-binding cassette subfamily C (ABCC), one of the largest superfamilies and transporter gene families of membrane proteins, is associated with various tumors. However, its relationship with liver hepatocellular carcinoma (LIHC) remains unclear.We used the Oncomine, UALCAN, Human Protein Atlas, GeneMANIA, GO, Kyoto Encyclopedia of Genes and Genomes (KEGG), TIMER, and Kaplan–Meier Plotter databases. On May 20, 2021, we searched these databases for the terms ABCC1, ABCC2, ABCC3, ABCC4, ABCC5, ABCC6, ABCC7, ABCC8, ABCC9, ABCC10, ABCC11, ABCC12, ABCC13, and “liver cancer.” The exposure group comprised LIHC patients, and the control group comprised normal patients (those with noncancerous liver tissue). All patients shown in the retrieval language search were included. We compared the mRNA expression of these proteins in LIHC and control patients to examine the potential role of ABCC1–13 in LIHC.Relative to the normal liver tissue, mRNA expression of ABCC1/2/3/4/5/6/10 was significantly upregulated (P < .001), and that of ABCC9/11 significantly downregulated (both P < .001), in LIHC. ABCC mRNA expression varied with gender (P < .05), except for ABCC11–13; with tumor grade (P < 0.05), except for ABCC7/12/13; with tumor stage (P < .05), except for ABCC11–13; and with lymph node metastasis status (P < .05), except for ABCC7/8/11/12/13. Based on KEGG enrichment analysis, these genes were associated with the following pathways: ABC transporters, Bile secretion, Antifolate resistance, and Peroxisome (P < .05). Except for ABCC12/13, the ABCCs were significantly associated with B cell, CD8+ T cell, CD4+ T cell, macrophage, neutrophil, and dendritic cell infiltration (P < .05). High mRNA expression of ABCC1/4/5/8 (P < .05) and low expression of ABCC6/7/9/12/13 (P < .05) indicated poor prognosis. Prognostic significance was indicated for ABCC2/13 for both men and women (P < .05); for ABCC1/6/12/13 for tumor grades 1–3 (P < .05); for ABCC5/11/12/13 for all tumor stages (P < .05); for ABCC1/11/12/13 for American Joint Committee on Cancer T stages 1–3 (P < .05); and for ABCC1/5/6/13 for vascular invasion. None showed prognostic significance for microvascular invasion (P < .05).We identified ABCC1/2/3/4/5/6/9/10/11 as potential diagnostic markers, and ABCC1/4/5/6/7/8/9/12/13 as prognostic markers, of LIHC. Our future work will promote the use of ABCCs in the diagnosis and treatment of LIHC.

SoundexGR: An algorithm for phonetic matching for the Greek language

AbstractText usually suffers from typos which can negatively affect various Information Retrieval and Natural Language Processing tasks. Although there is a wide variety of choices for tackling this issue in the English language, this is not the case for other languages. For the Greek language, most of the existing phonetic algorithms provide rather insufficient support. For this reason, in this paper, we introduce an algorithm for phonetic matching designed for the Greek language: we start from the original Soundex and we redesign and extend it for accommodating the Greek language’s phonetic rules, ending up to a family of algorithms, that we call ${\tt Soundex}_{GR}$ . Then, we report various experimental results showcasing how the algorithm behaves in different scenarios, and we provide comparative results for various parameters of the algorithm for revealing the trade-off between precision and recall in datasets with different kinds of errors. We also provide comparative results with matching using stemming, full phonemic transcription, and edit distance, that demonstrate that ${\tt Soundex}_{GR}$ performs better (indicatively, it achieves F-Score over 95% in collections of similar-sounded words). The simplicity, efficiency, and effectiveness of the proposed algorithm make it applicable and adaptable to a wide range of tasks.

Knowledge Distillation-Based Multilingual Code Retrieval

Semantic code retrieval is the task of retrieving relevant codes based on natural language queries. Although it is related to other information retrieval tasks, it needs to bridge the gaps between the language used in the code (which is usually syntax-specific and logic-specific) and the natural language which is more suitable for describing ambiguous concepts and ideas. Existing approaches study code retrieval in a natural language for a specific programming language, however it is unwieldy and often requires a large amount of corpus for each language when dealing with multilingual scenarios.Using knowledge distillation of six existing monolingual Teacher Models to train one Student Model—MPLCS (Multi-Programming Language Code Search), this paper proposed a method to support multi-programing language code search tasks. MPLCS has the ability to incorporate multiple languages into one model with low corpus requirements. MPLCS can study the commonality between different programming languages and improve the recall accuracy for small dataset code languages. As for Ruby used in this paper, MPLCS improved its MRR score by 20 to 25%. In addition, MPLCS can compensate the low recall accuracy of monolingual models when perform language retrieval work on other programming languages. And in some cases, MPLCS’ recall accuracy can even outperform the recall accuracy of monolingual models when they perform language retrieval work on themselves.