Field Of Information Retrieval 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.

Semantic Verification in Large Language Model-based Retrieval Augmented Generation

This position paper presents a novel approach of semantic verification in Large Language Model-based Retrieval Augmented Generation (LLM-RAG) systems, focusing on the critical need for factually accurate information dissemination during public debates, especially prior to plebiscites e.g. in direct democracies, particularly in the context of Switzerland. Recognizing the unique challenges posed by the current generation of Large Language Models (LLMs) in maintaining factual integrity, this research proposes an innovative solution that integrates retrieval mechanisms with enhanced semantic verification processes. The paper outlines a comprehensive methodology following a Design Science Research approach, which includes defining user personas, designing conversational interfaces, and iteratively developing a hybrid dialogue system. Central to this system is a robust semantic verification framework that leverages a knowledge graph for fact-checking and validation, ensuring the correctness and consistency of information generated by LLMs. The paper discusses the significance of this research in the context of Swiss direct democracy, where informed decision-making is pivotal. By improving the accuracy and reliability of information provided to the public, the proposed system aims to support the democratic process, enabling citizens to make well-informed decisions on complex issues. The research contributes to advancing the field of natural language processing and information retrieval, demonstrating the potential of AI and LLMs in enhancing civic engagement and democratic participation.

A deep neural network model for Chinese toponym matching with geographic pre-training model

ABSTRACT Multiple tasks within the field of geographical information retrieval and geographical information sciences necessitate toponym matching, which involves the challenge of aligning toponyms that share a common referent. The multiple string similarity approaches struggle when confronted with the complexities associated with unofficial and/or historical variants of identical toponyms. Also, current state-of-the-art approaches/tools to supervised machine learning rely on labeled samples, and they do not adequately address the intricacies of character replacements either from transliterations or historical shifts in linguistic and cultural norms. To address these issues, this paper proposes a novel matching approach that leverages a deep neural network model empowered by geographic language representation model, known as GeoBERT, which stands for geographic Bidirectional Encoder Representations from Transformers (BERT). This model harnesses the groundbreaking capabilities of the GeoBERT framework by extending a generalized Enhanced Sequential Inference Model architecture and integrating multiple features to enhance the accuracy and robustness of the toponym matching. We present a comprehensive evaluation of the proposed method’s performance using three extensive datasets. The findings clearly illustrate that our approach outperforms the individual similarity metrics used in previous studies.

SSR: Solving Named Entity Recognition Problems via a Single-stream Reasoner

Information Extraction (IE) focuses on transforming unstructured data into structured knowledge, of which Named Entity Recognition (NER) is a fundamental component. In the realm of Information Retrieval (IR), effectively recognizing entities can substantially enhance the precision of search and recommendation systems. Existing methods frame NER as a sequence labeling task, which requires extra data and, therefore may be limited in terms of sustainability. One promising solution is to employ a Machine Reading Comprehension (MRC) approach for NER tasks, thereby eliminating the dependence on additional data. This process encounters key challenges, including: (1) Unconventional predictions; (2) Inefficient multi-stream processing; (3) Absence of a proficient reasoning strategy. To this end, we present the Single-Stream Reasoner (SSR), a solution utilizing a reasoning strategy and standardized inputs. This yields a type-agnostic solution for both flat and nested NER tasks, without the need for additional data. On ten NER benchmarks, SSR achieved state-of-the-art results, highlighting its robustness. Furthermore, we illustrated its efficiency through convergence, inference speed, and low-resource scenario performance comparisons. Our architecture displays adaptability and can effortlessly merge with various foundational models and reasoning strategies, fostering advancements in both the IR and IE fields.

Music Genre Classification

The Music Genre Classification model automatically divides music into different genres using a small number of audio files and a range of musical attributes. This topic is highly relevant to the field of music information retrieval since it provides a way to organize and analyze large amounts of music files. For MGC, standard machine learning techniques such as SVM, KNN, Decision trees, and neural networks can be applied. These algorithms are trained to recognize different musical qualities and traits, which allows them to categorize the audio files into different genres. Numerous applications show that deep learning algorithms—such as CNN, ANN, and others—perform better than conventional machine learning algorithms. Consequently, the CNN method is adjusted to perform the categorization of music files. This classifies musical genres using deep learning methods from CNN. To evaluate the effectiveness of the MGC algorithms, accuracy is used. Moreover, the impact of different algorithms on MGC performance can be compared and studied. It can be applied to automated music recommendation systems, music production, and music education.

ADVANCMENTS IN TEXT SUMMARIZATION AND EXTRACTIVE QUESTION- ANSWERING : A MACHINE LEARNING APPROACH

In the era of social media platforms, the rapid expansion of data mining in the fields of information retrieval and natural language processing emphasizes the crucial need for automated text summarization. At the current time, pretrained word embedding techniques and sequence to sequence models can be effectively repurposed in the realm of social network summarization to efficiently condense significant information with strong encoding capabilities. However, dealing with the challenge of extended text dependency and efficiently utilizing latent topic mapping presents an increasingly significant obstacle for these computational models. In this document, we propose a topic- focused approach for both extractive and abstractive summarization, integrating Question Answering features by employing BERT and Pegasus pretrained models. A comprehensive analysis of the architectural complexities of the pre-existing models utilized in the creation of our model has been presented. The evaluation of our Text Summarization & Question Answering Model was conducted meticulously on a variety of datasets including Multi news, XSum, and CNN/Daily Mail, with experimental results indicating its achievement of state-of-the-art performance levels based on ROGUE scores. Following this, the verification of our results through human evaluation confirms that the summaries generated by our model are in alignment with the performance standards set by humans across various datasets. Furthermore, we have integrated an Extractive Question Answering task into our model, proposing an architecture leveraging BERT and conducting comparative analyses against alternative language models to gauge its efficacy. Keywords—Natural Language Processing (NLP), Text Summarization, Question & Answering (Q&A), Transformer Models, Pegasus, BERT (Bidirectional Encoder Representations from Transformers), Neural Topic Modeling, Extractive Summarization, Abstractive Summarization, Semantic Representation, Topic Embedding

Research Paper Summarization and Recommendation

The exponential growth of academic literature has made it increasingly challenging for researchers to keep up with the latest developments in their respective fields. This paper presents an approach for summarizing and recommending research papers to help researchers efficiently manage and navigate the vast amount of academic literature. We propose using a transformer-based model for generating abstractive summaries of research papers and a cosine similarity-based recommendation system for suggesting similar papers to a given paper. The proposed approach is evaluated on a dataset of research papers, and the results show that it is effective in generating coherent and concise summaries and recommending relevant research papers. The findings of this study have important implications for researchers and practitioners in the field of natural language processing and information retrieval.

A regularization algorithm for local key detection

In the field of music information retrieval, the detection of global key in both popular and classical music has been studied extensively, but local key detection has been studied to a lesser extent, even though modulation is an important component of compositional style. It is particularly challenging to identify key change boundaries correctly. We modeled this task as an optimization problem, that of finding out how to divide a piece into sections in different keys taking into consideration both the quality of the fit between the key and the section and the number of sections. We determined the optimal assignment of key to section using the Krumhansl–Schmuckler algorithm with a slightly modified version of the Krumhansl and Kessler key profile. We included a regularization algorithm in the formulation of our problem to balance the number of sections and avoid superfluous modulations. Using a dataset of 80 randomly chosen pieces of music in a variety of genres and levels of complexity, we compared our algorithm with a hidden Markov model (HMM) to determine which method is better for identifying local key. Our approach yielded significantly more accurate results and suggests future avenues of research.

Envisioning Information Access Systems: What Makes for Good Tools and a Healthy Web?

We observe a recent trend toward applying large language models (LLMs) in search and positioning them as effective information access systems. While the interfaces may look appealing and the apparent breadth of applicability is exciting, we are concerned that the field is rushing ahead with a technology without sufficient study of the uses it is meant to serve, how it would be used, and what its use would mean. We argue that it is important to reassert the central research focus of the field of information retrieval, because information access is not merely an application to be solved by the so-called ‘AI’ techniques du jour. Rather, it is a key human activity, with impacts on both individuals and society. As information scientists, we should be asking what do people and society want and need from information access systems and how do we design and build systems to meet those needs? With that goal, in this conceptual article we investigate fundamental questions concerning information access from user and societal viewpoints. We revisit foundational work related to information behavior, information seeking, information retrieval, information filtering, and information access to resurface what we know about these fundamental questions and what may be missing. We then provide our conceptual framing about how we could fill this gap, focusing on methods as well as experimental and evaluation frameworks. We consider the Web as an information ecosystem and explore the ways in which synthetic media, produced by LLMs and otherwise, endangers that ecosystem. The primary goal of this conceptual article is to shed light on what we still do not know about the potential impacts of LLM-based information access systems, how to advance our understanding of user behaviors, and where the next generations of students, scholars, and developers could fruitfully invest their energies.

“An Extensive study of Symantic and Syntatic Approaches to Automatic Text Summarization”

Automatic text summarization (ATS) has emerged as a crucial research domain in the discipline of natural language processing (NLP) and information retrieval. The exponential growth of digital content has necessitated the need for efficient techniques that can automatically generate concise and informative summaries from lengthy documents. This article provided a comprehensive recap of automatic text summarization, covering both abstractive and extractive methods. Using extractive techniques, prime phrases or keywords from the original text are identified and chosen, while abstractive methods involve producing summaries by paraphrasing and synthesizing content in a more human-like manner. Discussed the advantages and limitations of each approach, including the challenge of ATS, which arises when summarizing content from external sources. Furthermore, reviews common evaluation metrics used for assessing the quality of summaries and discusses recent advancements in neural network-based approaches for text summarization. This survey aims to provide an overview of automatic text summarization which acts as a useful resource for researchers and practitioners in the fields of information retrieval and NLP.

Research on the application of graph neural networks in text classification

With the rapid development of the Internet and social media, a large amount of text data is constantly generated, and text classification has become an important task. Text classification is an important branch in the field of natural language processing and has attracted the attention of many researchers in recent years. Due to the ease of editing text data, most texts in the network are manually constructed and uploaded by users. Therefore, the standardization of online texts and the classification of texts with different granularity are of great significance in the field of information retrieval. This article introduces the basic concepts of text classification and three text classification methods: knowledge engineering (rule) based text classification methods, traditional machine learning-based text classification methods, and deep neural network-based text classification methods. This article also analyzes the advantages and disadvantages of the three methods. Subsequently, the basic concepts and common models of graph neural networks were explored. An application case of graph neural networks in text classification is provided. This article provides effective suggestions for text classification applications in the field of natural language processing.

MCSSME: Multi-Task Contrastive Learning for Semi-supervised Singing Melody Extraction from Polyphonic Music

Singing melody extraction is an important task in the field of music information retrieval (MIR). The development of data-driven models for this task have achieved great successes. However, the existing models have two major limitations: firstly, most of the existing singing melody extraction models have formulated this task as a pixel-level prediction task. The lack of labeling data has limited the model for further improvements. Secondly, the generalization of the existing models are prone to be disturbed by the music genres. To address the issues mentioned above, in this paper, we propose a multi-Task contrastive learning framework for semi-supervised singing melody extraction, termed as MCSSME. Specifically, to deal with data scarcity limitation, we propose a self-consistency regularization (SCR) method to train the model on the unlabeled data. Transformations are applied to the raw signal of polyphonic music, which makes the network to improve its representation capability via recognizing the transformations. We further propose a novel multi-task learning (MTL) approach to jointly learn singing melody extraction and classification of transformed data. To deal with generalization limitation, we also propose a contrastive embedding learning, which strengthens the intra-class compactness and inter-class separability. To improve the generalization on different music genres, we also propose a domain classification method to learn task-dependent features by mapping data from different music genres to shared subspace. MCSSME evaluates on a set of well-known public melody extraction datasets with promising performances. The experimental results demonstrate the effectiveness of the MCSSME framework for singing melody extraction from polyphonic music using very limited labeled data scenarios.

Evolution and Emerging Trends in Musical Information Retrieval: A Comprehensive Review and Future Prospects

The rapid development of the digital music industry has brought challenges for music lovers and researchers. In response to these challenges, the field of Music Information Retrieval (MIR) emerged in the mid-1960s to capture the complicated and multi-layered nature of music. Among the various approaches explored, machine learning methods have shown promise in overcoming the complexity of this interdisciplinary field. This paper is primarily centered on conducting an in-depth review of the current state of research regarding the utilization of machine learning within the field of Music Information Retrieval (MIR). Additionally, it aims to forecast the potential future directions within the MIR industry. Upon extensive literature review, it becomes evident that various machine learning techniques, including Neural Networks (NN), Support Vector Machines (SVM), and K-nearest neighbors (KNN), have found common applicability in this field. Furthermore, this review highlights Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) as potential algorithms poised to shape the future landscape of MIR. The findings of this paper serve to elucidate the direction in which MIR is progressing, offering valuable guidance for forthcoming research and development endeavors. In doing so, it contributes to the continued progress and maturation of the field MIR.

How Does ChatGPT Use Source Information Compared With Google? A Text Network Analysis of Online Health Information.

How Does ChatGPT Use Source Information Compared With Google? A Text Network Analysis of Online Health Information.

The application of NLP in information retrieval

The field of Natural Language Processing (NLP) has experienced impressive advancements and has found diverse applications. This paper presents a comprehensive review of the development of NLP in the field of information retrieval. It explores different stages of NLP techniques and methods, including keyword matching, rule-based approaches, statistical methods, and the utilization of machine learning and deep learning technologies. Furthermore, the paper provides detailed insights into the specific applications of NLP in domains such as academic information retrieval, medical information retrieval, travel information retrieval, and e-commerce information retrieval. It analyzes the current state of NLP applications in these domains, highlights their advantages, and discusses their associated limitations. Finally, the paper emphasizes the continuous advancement of the NLP field, with a particular focus on semantic understanding, personalized retrieval, and multimodal information retrieval, to better adapt to diverse data types and user requirements. The paper concludes by summarizing the main points discussed and providing future directions.

Programming Methods for Industrial Robotics and Expanding Applications

Industrial robotics industry is presently experiencing significant growth and is generally recognized as a crucial element within the industrial sector. The technology offered by this system is standardized and well-suited for a wide range of automated operations. This research investigates the industrial robotics industry and its use of standardized technologies to automate diverse operational procedures. This article explores the two primary tactics used in the process of robotization, with the diverse levels of cooperation seen between human beings and robots. The present study examines the control and programming approaches used in the field of information retrieval, together with the notable technological advancements that have arisen within this area. Moreover, it incorporates the many challenges and limitations faced during the installation of automated industrial robot systems. This research places particular emphasis on the use of computer vision-based approaches, deep reinforcement learning techniques, simulations, and synthetic data within the domain of industrial robotics. The article ends by providing an analysis of novel control methodologies and the use of external coordinators in the programming of industrial robots.

Information Retrieval Evaluation Measures Defined on Some Axiomatic Models of Preferences

Information retrieval (IR) evaluation measures are essential for capturing the relevance of documents to topics and determining the task performance efficiency of retrieval systems. The study of IR evaluation measures through their formal properties enables a better understanding of their suitability for a specific task. Some works have modeled the effectiveness of retrieval measures with axioms, heuristics, or desirable properties, leading to order relationships on the set where they are defined. Each of these ordering structures constitutes an axiomatic model of preferences (AMP), which can be considered as an “ideal” scenario of retrieval. Based on lattice theory and on the representational theory of measurement, this work formally explores numeric, metric, and scale properties of some effectiveness measures defined on AMPs. In some of these scenarios, retrieval measures are completely determined from the scores of a subset of document rankings: join-irreducible elements. All the possible metrics and pseudometrics, defined on these structures are expressed in terms of the join-irreducible elements. The deduced scale properties of the precision, recall, F -measure, RBP , DCG , and AP confirm some recent results in the IR field.

Music emotion classification using a hybrid CNN-LSTM model

The emotional content of music, interwoven with the intricacies of human affect, poses a unique challenge for computational recognition and classification. With the digitalization of music libraries expanding exponentially, there is a pressing need for precise, automated tools capable of navigating and categorizing vast musical repositories based on emotional contexts. This study advances music emotion classification in the field of music information retrieval by developing a deep learning model that accurately predicts emotional categories in music. The goal of this research is to advance the field of music emotion classification by leveraging the capabilities of convolutional neural networks combined with long short-term memory within deep learning frameworks. The contribution of this study is to provide a refined approach to music emotion classification, combining the power of convolutional neural networks and long short-term memory architectures with sophisticated preprocessing of the Emotify dataset for a deeper and more accurate analysis of musical emotions. The research introduces a novel architecture combining Convolutional Neural Networks and Long Short-Term Memory networks designed to capture the intricate emotional nuances in music. The model leverages convolutional neural networks for robust feature detection and Long Short-Term Memory networks for effective sequence learning, addressing the temporal dynamics of musical features. Utilizing the Emotify dataset, comprising tracks annotated with nine emotional features, the study expands the dataset by segmenting each track into 20 parts, thereby enriching the variety of emotional expressions. Techniques like the synthetic minority oversampling technique were implemented to counter dataset imbalance, ensuring equitable representation of various emotions. The spectral characteristics of the samples were analyzed using the Fast Fourier Transform, contributing to a more comprehensive understanding of the data. Through meticulous fine-tuning, including dropout implementation to prevent overfitting and learning rate adjustments, the developed model achieved a notable accuracy of 94.7 %. This high level of precision underscores the model's potential for application in digital music services, recommendation systems, and music therapy. Future enhancements to this music emotion classification system include expanding the dataset and refining the model architecture for even more nuanced emotional analysis

Augmentation Embedded Deep Convolutional Neural Network for Predominant Instrument Recognition

Instrument recognition is a critical task in the field of music information retrieval and deep neural networks have become the dominant models for this task due to their effectiveness. Recently, incorporating data augmentation methods into deep neural networks has been a popular approach to improve instrument recognition performance. However, existing data augmentation processes are always based on simple instrument spectrogram representation and are typically independent of the predominant instrument recognition process. This may result in a lack of coverage for certain required instrument types, leading to inconsistencies between the augmented data and the specific requirements of the recognition model. To build more expressive instrument representation and address this inconsistency, this paper constructs a combined two-channel representation for further capturing the unique rhythm patterns of different types of instruments and proposes a new predominant instrument recognition strategy called Augmentation Embedded Deep Convolutional neural Network (AEDCN). AEDCN adds two fully connected layers into the backbone neural network and integrates data augmentation directly into the recognition process by introducing a proposed Adversarial Embedded Conditional Variational AutoEncoder (ACEVAE) between the added fully connected layers of the backbone network. This embedded module aims to generate augmented data based on designated labels, thereby ensuring its compatibility with the predominant instrument recognition model. The effectiveness of the combined representation and AEDCN is validated through comparative experiments with other commonly used deep neural networks and data augmentation-based predominant instrument recognition methods using a polyphonic music recognition dataset. The results demonstrate the superior performance of AEDCN in predominant instrument recognition tasks.

Multifractal Characterization of Texts for Pattern Recognition: On the Complexity of Morphological Structures in Modern and Ancient Languages.

The study of languages' structure and their organization in a set of well-defined relation schemes is a delicate matter. In the last decades, the convergence of traditional conflicting views by linguists is supported by an interdisciplinary approach that involves not only genetics or bio-archelogy but nowadays even the science of complexity. In light of this new and useful approach, this study proposes an in-depth analysis of the complexity underlying the morphological organization, in terms of multifractality and long-range correlations, of several modern and ancient texts pertaining to various linguistic strains (including ancient Greek, Arabic, Coptic, Neo-Latin and Germanic languages). The methodology is grounded on the mapping procedure between lexical categories belonging to text excerpts and time series, which is based on the rank of the frequency occurrence. Through the well-known MFDFA technique and a specific multifractal formalism, several multifractal indexes are then extracted for characterizing texts and the multifractal signature has been adopted for characterizing several language families, such as Indo-European, Semitic and Hamito-Semitic. The regularities and differences in the linguistic strains are assessed within a multivariate statistical framework and corroborated with a Machine Learning approach that is dedicated, in turn, to investigate the predictive power of the multifractal signature pertinent to text excerpts. The obtained results show a strong presence of persistence, i.e., memory, in the morphological structure of analyzed texts and we claim that this property has a role in characterizing the studied linguistic families. In fact, for example, the proposed analysis framework - grounded on complexity indexes - is able to easily distinguish ancient Greek texts from Arabic ones, as they belong to different language strains, i.e., indo-European and Semitic, respectively. The proposed approach has been proven effective and can be adopted for further comparative studies and for designing new informetrics for further advances in the fields of information retrieval and Artificial Intelligence.