Information Retrieval Technology Research Articles

Exploring the fusion of natural language processing and information retrieval

The ongoing progress of technology has led to a strong focus on the merging of Natural Language Processing (NLP) and Information Retrieval (IR) in current research. This paper provides a comprehensive analysis of the fundamental concepts, importance, and difficulties encountered in the domains of NLP and IR, examining their effects on real-world applications. By examining the present state of NLP technology in information retrieval, it is evident that the advancement of NLP technology has introduced fresh opportunities for information retrieval, such as the NLPIR model. However, it also encounters problems in terms of adaptability and generalization capacity. Future study should prioritize enhancing the precision and efficiency of NLP technology, as well as investigating its suitability and adaptability in dealing with certain domains or languages. By consistently striving and introducing new ideas, the fields of NLP and information retrieval will have a promising future, offering individuals more convenient and precise information retrieval services.

Personalized recommendation of entertainment robots in fine arts education based on human–computer interaction and data mining

In the current digital entertainment environment, the art learning mode of human–computer interaction can effectively enhance students’ interest. Entertainment robots, as a novel personalized interaction tool, can provide a more intelligent and entertaining way for art teaching. This article analyzes the personalized recommendation of entertainment robots in art education based on human–computer interaction and data mining.On the basis of collecting a large number of fine art education resources and organizing and labeling them, data mining technology is used to extract user background information and interest preferences. Information retrieval technology is used to filter out content related to user interests from the massive art education resources based on the extracted user interests. We also use recommendation algorithms to sort and personalize the recommended content based on the user’s historical behavior and other user evaluations, in order to improve the accuracy of recommendation results and user satisfaction. The effectiveness and practicality of this personalized recommendation model were verified through user surveys and data analysis. The experimental results indicate that this model can quickly and accurately recommend suitable fine art education content for users, improving their learning effectiveness and experience.

Personalized music teaching service recommendation based on sensor and information retrieval technology

In order to fully utilize the existing resources of music information, people have found that music information retrieval technology has important research significance for personalized music teaching. Overall, music information retrieval technology is still in the experimental exploration stage and lacks technical and practical systems. In this context, this article proposes recommendation algorithms that can effectively adjust the content of information services based on user preferences. However, recommendation algorithms are still in an immature and complex stage, and there are still issues with their accuracy. In response to the issue of recommendation accuracy, this article utilizes information retrieval technology to optimize recommendation algorithms. Combine these two algorithms for recommendation. The main steps that affect recommendation results in recommendation algorithms include similarity calculation, nearest neighbor selection method, and score prediction method calculation. The experimental results indicate that the proposed algorithm can effectively solve the existing problems of users, and can also accurately retrieve the content of interest, greatly improving the diversity of the proposed content. We use utilizing algorithmic learning to create a personalized music teaching system that enables students to independently advance towards their self-development goals, ultimately achieving the goal of promoting the harmonious development of students' personalities.

Application and Research of Media Information Retrieval Technology in Student Work Management Systems

The development of the internet has promoted the improvement of media information retrieval technology, which has also been applied to the work management of college students. This paper mainly adopts media information retrieval technology, studies the function principle of this technology, and establishes a student work management system based on media information retrieval technology. Then, this system is applied to the student management of different grades, and the problems existing in the student work management of each grade are compared and analyzed, and the corresponding improvement measures are put forward. The results indicate that the student work management system can improve the efficiency of student work management among colleges and universities in different grades.

A Case Study of Millennial English Teachers’ Awareness of Digital Media in EFL Classrooms

Digital literacy embodies the competence in effectively employing technology for information retrieval, appraisal, creation, and transmission. This case study not only explores the digital literacy of millennial English teachers but uniquely unveils the seamless integration of a diverse array of digital media into their EFL classrooms. The questionnaire served as a means to gauge the extent of the millennial English teachers understood the notion of digital literacy. Meanwhile, to understand how they use digital technology resources in their instructional design and pedagogical strategies, this study deployed observation, interview, and document analysis. Ten English teachers from Cianjur who belong to the millennial generation enlisted in the study. The findings underscored that the participants adeptly utilized an array of digital technology tools in their day-to-day professional undertakings. They have demonstrated proficiency with digital teaching tools and have seamlessly integrated them into their EFL classroom. Nonetheless, there is a discernible need for further support in advancing their digital literacy skills, and further, collaborative environment to explore innovative pedagogical approaches. Also, a sufficient amount of time for professional development is necessary, as are policies that promote the effective use of mobile devices in the classroom. Furthermore, it is imperative to stimulate the teachers’ experimentation with emerging technologies and elevate their digital literacy levels. Therefore, it is advised that government and policymakers proffer substantial, reliable, and easily accessible technical support tailored to the needs of millennial English teachers to continue their digital literacy growth.

Development Path of Teaching Method Innovation in Art Colleges and Universities under the Background of Internet

Abstract The informatization teaching method is a big challenge to the traditional form of teaching in art college education. This paper studies the construction of an informatization teaching method combining multimedia technology and multimodal media information retrieval technology. In view of the demand and development conception of teaching methods in art colleges and universities under the background of the Internet, it examines the willingness of teachers of art colleges and universities to teach informatization as well as the degree of satisfaction of their teaching ability. It optimizes the retrieval of learning resources based on the multi-category classification method of ELM classifiers and proposes an integrated mode of informatization teaching in art disciplines. According to the factors affecting the informatization teaching ability of art teachers, teaching design and teacher implementation are carried out in a case-by-case manner to analyze and summarize the program effects of the informatization teaching approach. The correlation coefficient between teachers’ willingness to adopt informative teaching methods and their satisfaction with their information teaching ability is 0.614, which is moderately correlated. After the implementation of the teaching program, the total score of the teacher’s informatization teaching ability reaches 80.052, which improves the level of informatization teaching compared to the level of informatization in pre-preparation. The informatization teaching method’s program design can enhance the teacher’s informatization teaching level, which can have an impact on the innovative development of the teaching method in art colleges and universities through practical experience.

The Application of Artificial Intelligence Technology in Public Library Information Retrieval

With the continuous innovation and development of social science and technology, artificial intelligence technology has been the focus of attention and research in more and more industries. Public library, as an important place in the society to provide mass education for the masses, should fully seize the development opportunity with artificial intelligence technology as the core idea in the operation and development, carry out intelligent, automatic and digital reform and innovation of the library, and optimize the links of information retrieval, book borrowing, information service and access to the library. Through Internet technology, artificial intelligence technology and other advanced technologies, the whole science and technology content of public libraries and public service quality is improved so as to meet the specific needs of the public for the use of public education resources. Based on this, this paper first briefly describes the basic concepts of human intelligence technology and information retrieval technology, and then studies the specific application of artificial intelligence technology in public library information retrieval link and related service applications, hoping to provide some reference for professionals.

Analysis of the effect of user-understanding-based retrieval system improvement on the application of intelligent archive management

Abstract As a precipitation and accumulation of history, archives management has gradually tended to be digitalized and informalized with the continuous updating and development of modern technology. In this paper, we first study the retrieval system and focus on the Boolean model, vector model, and probabilistic model in information retrieval technology. The matching relationship between documents and queries is detected from the document set for the user’s query, and a relevance retrieval system based on user understanding is proposed to solve the matching problem. The amount of information that needs to be retrieved is growing exponentially, and how a user perceives the information is crucial to the process. Then, in order to solve the problem of insufficient retrieval efficiency caused by the explosive growth of wisdom files, the retrieval system is creatively optimized on the basis of the ant colony algorithm, which effectively improves the efficiency of wisdom file management. The efficiency of the optimized retrieval system is verified and analyzed in an experimental simulation environment. The findings demonstrate that when the amount of archives rises, the retrieval effectiveness of the improved ant colony algorithm described in this study marginally improves, but in 10~35s. As the inventory of the Smart Archives increases, the content retrieval of the archives will become more and more frequent. This study improves retrieval efficiency and serves as a good demonstration for the construction of archival management information technology.

Developing an ontology-based system for semantic processing of scientific digital libraries

The development of theories, methods, and algorithms for the discovery and formation of new knowledge remains one of the most important tasks for any researcher, especially if they are actively working to create new scientific publications. Yet, there is no universal language to describe full formal concepts (i.e. knowledge) or the systemology of transdisciplinary scientific research. Because of this, researchers face a set of urgent challenges. One such challenge is how to speed up the process of finding information in their own sources. To address this challenge, we created an ontology-related system for processing digital libraries of scientific publications. This system implements the technologies of information retrieval and knowledge discovery in digital libraries with an emphasis on technologies and instruments such as those used in the Semantic Web and cognitive graphics.

Using Multimedia Information Retrieval Technology to Optimize the Strategies of College English Teaching

Abstract In the design of college ELT(English Language Teaching), it is the most important link to create situations conducive to learners’ meaning construction. At present, the starting point of college ELT is no longer simply and mechanically teaching words, sentences, and grammar, but based on language tools, which improves students’ cultural quality, personality, and professional knowledge. Therefore, this paper makes a research on college ELT through multimedia information retrieval technology. The research shows that 38.40% of the students think that the use of multimedia in teaching can stimulate their interest in learning. 14.66% of the students believe that they can effectively stimulate their interest in learning through extracurricular activities in English. Only 7.38% of the students felt that teachers should encourage and help them. Some students think that emphasizing the importance of English learning can stimulate their interest in learning. At present, the English classroom is more scientific, orderly, diversified, and systematic, which provides convenience for the new curriculum reform being implemented. This paper discusses ELT under multimedia information retrieval technology and puts forward predictive strategies for the problems that may arise now and in the future.

Information and Communication Technologies in the Development of Online Media in China

The research objective is to take multimedia technology as an example, analyze the role of information and communication technology in different fields, and explain the application of information and communication technology. In order to achieve this goal, the following research objectives have been established: 1. Explore the multifaceted development of multimedia technology. 2. Research and analyze the impact of information retrieval technology on multimedia technology. 3. By analyzing and comparing the research objects of information technology and multimedia technology. The research topic is the application of information and communication technology in the development of Chinese online media. Research methods: literature research, Content analysis, comparative analysis, thematic analysis. Possible practical applications: mass media, scientific fields. Information and communication technology has changed people's traditional media development model, providing people with many convenient, comprehensive, and diverse services, and has a wide range of applications in various fields of society. The development of Chinese online media has shown trends such as universality, intelligence, and speed. We must fully understand the characteristics and advantages of Chinese online media development, and apply information and communication technology to various fields to create more and greater value.

Supervised Contrastive Learning with Term Weighting for Improving Chinese Text Classification

With the rapid growth of information retrieval technology, Chinese text classification, which is the basis of information content security, has become a widely discussed topic. In view of the huge difference compared with English, Chinese text task is more complex in semantic information representations. However, most existing Chinese text classification approaches typically regard feature representation and feature selection as the key points, but fail to take into account the learning strategy that adapts to the task. Besides, these approaches compress the Chinese word into a representation vector, without considering the distribution of the term among the categories of interest. In order to improve the effect of Chinese text classification, a unified method, called Supervised Contrastive Learning with Term Weighting (SCL-TW), is proposed in this paper. Supervised contrastive learning makes full use of a large amount of unlabeled data to improve model stability. In SCL-TW, we calculate the score of term weighting to optimize the process of data augmentation of Chinese text. Subsequently, the transformed features are fed into a temporal convolution network to conduct feature representation. Experimental verifications are conducted on two Chinese benchmark datasets. The results demonstrate that SCL-TW outperforms other advanced Chinese text classification approaches by an amazing margin.

Leveraging multi-level embeddings for knowledge-aware bug report reformulation

Software bug analysis based on the information retrieval (IR) technology is widely studied and used for bug understanding, localization and fixing. IR technology with various textual feature extraction methods formulates the textual information in a given new bug report (i.e., title and description) as an initial query. However, due to the low-quality content in the new bug report and improper representation to be used as a query, the retrieval results are usually not satisfactory.To alleviate these problems, we propose a novel knowledge-aware bug report reformulation approach (a.k.a, KABR) by leveraging multi-level embeddings from the bug data. First, we construct a bug-specific knowledge graph (KG) to manage and reuse prior knowledge extracted from historical bug reports. Then, we extract word embedding from the original bug data, entity embedding and context embedding from the bug-specific KG to enhance the initial query. Finally, a new query representation is generated by leveraging multi-level embeddings through Convolutional Neural Networks (CNN) with the self-attention mechanism. We evaluate KABR based on the duplicate bug report detection task, and the experimental results show that KABR achieves 6%–11% F1-measure improvement over the state-of-the-art approaches.

Information Retrieval Technologies and Big Data Analytics to Analyze Product Innovation in the Music Industry

In Cultural and Creative Industries, innovation contributes to generating a competitive advantage thanks to the fundamental role assumed by the human creativity and the quest for novelty. In particular, the music industry stands out as one of the most successful ones, in terms of both revenue and employment. The music industry is also quickly and constantly growing, supported by the new digital technologies and the rise of streaming platforms and digital services, which have increased the availability of continuous, reliable, and timely data. Consequently, this may allow the implementation of novel techniques to study product innovation occurring in the music industry. Nonetheless, quantitative approaches to study innovation in this industry are scant. The present study aims at filling this gap by developing a quantitative approach to analyze product innovation dynamics in the music industry exploiting data collected through Music Information Retrieval technologies. We selected a successful band as a case study and analyzed each song released from 1984 to 2016 to obtain a quantitative representation of their musical production. We then developed and applied quantitative similarity metrics to see how each album was similar or different from the previous ones and from the most relevant music genres, to better understand innovation dynamics in music production.

Boyko Ontological system processing of databases of scientific publications

Development of theories, methods and algorithms for the discovery and formation of new knowledge always was one of the most important tasks for any researcher, especially if they actively working on creation of new scientific publications. There is no universal language to describe formally concepts (knowledge) and systemology of transdisciplinary scientific research. Because of this, researchers have a set of urgent problems, and one of them is the way of speeding up the process of finding information (in the form of cognitive-structure) in their own sources. Ontological system for processing of databases of scientific publications created to solve this problem for a researcher, who have from tens to hundreds of scientific papers published. We are unaware of search systems, which would provide the same information for a researcher in such a short time. Ontological system implements technologies of Information Retrieval and Knowledge Discovery in Databases with accent on technologies and instruments such as Semantic Web and cognitive graphics. Development of such ontological system have three stages. On the first stage instruments for system development created, methods and algorithms of interaction between system components "User ¾ Knowledge engineer ¾ Remote endpoint", also data added to the system at this stage. On the second stage task of multimedia presentation for conceptual and figurative structures, described in scientific documents, solved. Gaining new knowledge problem solved on the third stage.

Mobile Health Text Misinformation Identification Using Mobile Data Mining

More than six million people died of the COVID-19 by April 2022. The heavy casualties have put people on great and urgent alert, and people have tried to find all kinds of information to keep them from being infected by the coronavirus. This research tries to find out whether the mobile health text information sent to people's devices is correct as smartphones have become the major information source for people. The proposed method uses various mobile information retrieval and data mining technologies including lexical analysis, stopword elimination, stemming, and decision trees to classify the mobile health text information to one of the following classes: (1) true, (2) fake, (3) misinformative, (4) disinformative, and (5) neutral. Experiment results show the accuracy of the proposed method is above the threshold value 50% but is not optimal. It is because the problem, mobile text misinformation identification, is intrinsically difficult.

SBugLocater: Bug Localization Based on Deep Matching and Information Retrieval

Bug localization is a technology that locates buggy source files using bug reports reported by users. Automatic localization of buggy files can speed up the process of bug fixing to improve the efficiency and productivity of software quality assurance teams. Nowadays, some research studies have investigated the natural language information retrieval technology, but few of them have applied the matching technology in deep learning to bug localization. Therefore, we propose a bug localization model SBugLocater based on deep matching and IR. The model composes of three layers: semantic matching layer, relevance matching layer, and IR layer. In particular, the relevance matching layer captures fine-grained local matching signals, while coarse-grained semantic similarity signals come from the semantic matching layer. Further, based on collaborative filtering in different directions, the IR layer works to find whether bug reports and source files are related, which indirectly transforms the matching task of different grammatical structures between bug reports and source files into the same structure and solves the mismatching problem of the first two matching models when the query is short. In our work, four benchmark data sets are used as experimental data sets and Accuracy@k, MAP, and MRR as evaluation metrics, which are used to compare and analyze the performance of bug localization with the four state-of-the-art methods. Experimental results show that SBugLocater outperforms the four models. For example, compared with the best of the four models, the evaluation metrics of Accuracy@10, MAP, and MRR are improved by 6.9%, 13.9%, and 17%, respectively.

Upgrading and Optimization of Ski Tourism Resort Using Data Mining and Data Fusion

After years of development, the ski industry has formed a prototype of a ski tourism industry cluster integrating skiing, skiing entertainment, skiing technology, and skiing culture. With the rapid development of global information, data mining and data fusion have attracted extensive attention as high-tech technologies for extensive data processing and useful information retrieval. The principles of these two data processing technologies are different, but they are functionally compatible with each other. This paper will use the methods of data mining and data fusion to research and analyze the upgrading and optimization of ski tourism destinations; eliminate unnecessary information; reduce rules; establish a fusion system according to the basic rules of rough sets; use industrial cluster theory, industrial structure and optimization theory, industrial economy, and sports tourism management theory to study the current situation and existing problems of ski tourism; and analyze the mechanism and theory of upgrading and optimization of ski tourism industry structure based on industrial clusters. The results show that the per capita ecological footprint of the ski resort has decreased by 0.06178 hm2 in the past five years, and the total ecological footprint of the whole region in 2020 is 535089.3 hm2. Compared with 2016, it increased by 0.97%, exceeding the ecological deficit carrying supply by 5 times. Compared with traditional algorithms, the performance of data mining and data fusion algorithms is improved by 48%. It is concluded that the development strategies of the ski tourism industry include the innovation of ice and snow sports products, the innovation of technology development paths, the optimization of the management mode and marketing mode of the ski tourism industry, and the innovation of industrial systems and systems.

Extracting Searching as Learning Tasks Based on IBRT Approach

With the rapid development of the World Wide Web and information retrieval technology, learning supported by searching engines (such as making travel plans) has boomed over the past years. With the help of search engines, learners can easily retrieve and find large amounts of information on the web. Recent research in the searching as learning (SAL) area has associated web searching with learning. In SAL processes, web learners recursively plan tasks, formulate search queries, obtain information from web pages, and change knowledge structures, to gradually complete their learning goals. To improve the experiences of web learners, it is important to accurately present and extract tasks. Using learning styles and similarity metrics, we first proposed an IBRT model to implement structured representations of the SAL process for each learner. SAL tasks were then extracted from the structures of IBRT. In this study, a series of experiments were carried out against assignment datasets from the Northeastern University (China) UWP Programming Course. Comparison results show that the proposed method can significantly improve the performance of SAL task extraction.

Text Complexity Classification Data Mining Model Based on Dynamic Quantitative Relationship between Modality and English Context

With the rapid development of mobile internet technology, there are a large number of unstructured data in dynamic data, such as text data, multimedia data, etc., so it is essential to analyze and process these unstructured data to obtain potentially valuable information. This article first starts with the theoretical research of text complexity analysis and analyzes the source of text complexity and its five characteristics of dynamic, complexity, concealment, sentiment, and ambiguity, combined with the expression of user needs in the network environment. Secondly, based on the specific process of text mining, namely, data collection, data processing, and data visualization, it is proposed to subdivide the user demand analysis into three stages of text complexity acquisition, recognition, and expression, to obtain a text complexity analysis based on text mining technology. After that, based on computational linguistics and mathematical-statistical analysis, combined with machine learning and information retrieval technology, the text in any format is converted into a content format that can be used for machine learning, and patterns or knowledge are derived from this content format. Then, through the comparison and research of text mining technology, combined with the text complexity analysis hierarchical structure model, a quantitative relationship complexity analysis framework based on text mining technology is proposed, which is embodied in the use of web crawler technology. Experimental results show that the collected quantitative relationship information is identified and expressed in order to realize the conversion of quantitative relationship information into product features. The market data and text data can be integrated to help improve the model performance and the use of text data can further improve predictions for accuracy.