Computer Information Retrieval Research Articles

Financial Statement Text Information Mining and Key Information Extraction Model Construction

Financial statement text information mining and key information extraction model design are critical areas of research that aim to use advanced computational approaches to extract important insights from textual data contained in financial documents. In this work, they look at methodologies, techniques, and applications that combine natural language processing (NLP) and machine learning to automate financial statement interpretation. To lay the groundwork for the research, researchers first conduct a thorough examination of existing literature in interdisciplinary domains such as computational linguistics, information retrieval, and finance. Building on insights from earlier studies, they design and use unique NLP approaches, such as named entity identification, syntactic parsing, sentiment analysis, and topic modelling, to extract essential financial metrics from textual data. Additionally, they create machine learning models that are suited to the peculiarities of financial terminology and reporting standards, combining domain-specific knowledge with linguistic experience to improve accuracy and reliability. They demonstrate the efficacy and scalability of the technique in automating the extraction of crucial financial information, such as revenue trends, cost patterns, and risk factors, through rigorous testing on real-world financial data. These results highlight the transformative power of natural language processing and machine learning in financial analysis, providing stakeholders in finance and accounting with actionable intelligence for informed decision-making, risk assessment, and compliance monitoring. By bridging the gap between computational linguistics and financial analysis, this study advances financial text analysis and provides the framework for future research and innovation in this emerging field.

Computational Technologies for Fashion Recommendation: A Survey

Fashion recommendation is a key research field in computational fashion research and has attracted considerable interest in the computer vision, multimedia, and information retrieval communities in recent years. Due to the great demand for applications, various fashion recommendation tasks, such as personalized fashion product recommendation, complementary (mix-and-match) recommendation, and outfit recommendation, have been posed and explored in the literature. The continuing research attention and advances impel us to look back and in-depth into the field for a better understanding. In this article, we comprehensively review recent research efforts on fashion recommendation from a technological perspective. We first introduce fashion recommendation at a macro level and analyze its characteristics and differences with general recommendation tasks. We then clearly categorize different fashion recommendation efforts into several sub-tasks and focus on each sub-task in terms of its problem formulation, research focus, state-of-the-art methods, and limitations. We also summarize the datasets proposed in the literature for use in fashion recommendation studies to give readers a brief illustration. Finally, we discuss several promising directions for future research in this field. Overall, this survey systematically reviews the development of fashion recommendation research. It also discusses the current limitations and gaps between academic research and the real needs of the fashion industry. In the process, we offer a deep insight into how the fashion industry could benefit from the computational technologies of fashion recommendation.

The Renaissance Scholar of Library and Information Science: Professor Linda C. Smith

The Renaissance Scholar of Library and Information Science:Professor Linda C. Smith Anita S. Coleman (bio) and Martha Kyrillidou (bio) This collection of essays is to honor Linda C. Smith, widely recognized as a well-established and dedicated teacher and scholar at the University of Illinois Urbana-Champaign and the soul of the iSchool. Smith's contributions to library and information science (LIS) research, teaching, and higher education administration, as an award-winning professor of LIS for the past forty-five years, have been characterized by attention to interdisciplinarity, people, and information technology. From the very beginning of her graduate studies, Smith targeted key aspects of the information revolution that would come to shape our technological world. Her impact on education for the information professions as well as on generations of graduate students has been a richly complex and multithreaded story. It is thus fitting that an issue in her honor recognize and laud Professor Smith as the renaissance scholar of LIS, trace the arc of a relevant past, and reflect on a future that Smith so presciently studied. Smith's oeuvre includes her movement from pretenure basic research in a new area to the integration and transformation of that knowledge through research, teaching, and service as a distinguished and tenured professor. In her early work, Smith focused on how artificial intelligence techniques could be used to improve computerized information retrieval systems. Captured initially by the proliferation of databases and the impact they had on reference work and information behavior in libraries, she co-edited five editions of the key textbook in this area, Reference and Information Services: An Introduction. She has written one of the most highly cited articles on citation analysis (1981), which is also one of the most highly cited articles ever published in Library Trends. Among her many articles and contributions to the research and professional literature, she coedited a Festschrift in honor of her colleague Kathryn Luther Henderson, a book on technical services management (Smith and Carter 1996). Finally, she [End Page 1] has supported numerous students, both domestic and international, in pursuing their passions and achieving their goals. It was Smith's support for her PhD students that brought together the two co-editors of this special issue. Those who know Linda Smith, especially her doctoral students, are cognizant that her editorial skills are unparalleled. As coeditors, we are thankful for her willingness to read and review the contributed articles as they came in. Such meticulous, and often invisible, editorial expertise is the kind of added support that Linda has generously and diligently provided her collaborators, colleagues, students, the iSchool and the University of Illinois over the many years of her work as a researcher. This Festschrift is a testament to Linda Smith. It is done for her and with her. The articles in Linda's Festschrift represent the countless ways she has contributed to the discipline of LIS and helped to structure its evolution and growth as a field of knowledge and practice. "Library and Information Science, Interdisciplinary Perspectives" is a collection of essays based on three central themes of Smith's prodigious work: library systems, intelligent and interdisciplinary services, and information sciences education. Smith's themes were selected from her writings, such as her very first research paper, "Systematic Searching of Abstracts and Indexes in Interdisciplinary Areas," which won the 1974 ASIS Student Paper Award and was published in the Journal of the American Society for Information Science. While emerging trends were her motivation, Smith's goal for her research was to build capable computer systems and save the time of researchers and information users. That goal never changed, and it led her to study the different applications of computers beyond the sciences to humanities, engineering, and medicine. In the process she also became an astute administrator and a key influencer of LIS education. The topics covered in this issue include LIS education, accreditation, teaching reference and bibliographic searching, navigating copyright, and serials modeling, concluding with admonitions for new roles in LIS in relation to data stewardship and interdisciplinarity and in relation to artificial intelligence and the responsibilities of LIS roles. Linda's Design Lynne C. Howarth and Eileen G...

Robust pattern for face recognition using combined Weber and pentagonal-triangle graph structure pattern

Face recognition is a challenging task in computer vision and information retrieval due to the large variation in facial images in terms of pose, expression and illumination. These challenges motivate us to propose a robust pattern for face recognition using combined Weber pattern (CWP) and pentagonal-triangle graph structure pattern (PTGSP). Firstly, abrupt intensity variations in facial images are removed by multiblock operation. Further, by exploring the relationship among center, neighboring and adjacent pixels, CWP consisting of three unique Weber patterns is computed. In order to obtain the pattern that is more robust to facial variations, PTGSP has been computed on the facial images. PTGSP covers the widely used features of face images. A feature set of high dimension is produced by combining the features of CWP and PTGSP. Dimensionality of the proposed feature and also variance within class is reduced through principal component analysis (PCA) plus linear discriminant analysis (LDA) algorithm. The classification performance of the proposed method is compared with various state-of-the-art methods on variety of benchmark face datasets with variation in pose, expression, illumination and occlusion. Experiments on FEI, Georgia, ORL, Yale and Faces94 databases clearly prove the robustness of the proposed method in contrast to existing handcrafted feature extraction techniques.

From Keyness to Distinctiveness – Triangulation and Evaluationin Computational Literary Studies

From Keyness to Distinctiveness – Triangulation and Evaluation<b>in Computational Literary Studies</b>

Machine Learning for Additive Manufacturing

Additive manufacturing (AM) is the name given to a family of manufacturing processes where materials are joined to make parts from 3D modelling data, generally in a layer-upon-layer manner. AM is rapidly increasing in industrial adoption for the manufacture of end-use parts, which is therefore pushing for the maturation of design, process, and production techniques. Machine learning (ML) is a branch of artificial intelligence concerned with training programs to self-improve and has applications in a wide range of areas, such as computer vision, prediction, and information retrieval. Many of the problems facing AM can be categorised into one or more of these application areas. Studies have shown ML techniques to be effective in improving AM design, process, and production but there are limited industrial case studies to support further development of these techniques.

Optimal realizations and the block decomposition of a finite metric space

Finite metric spaces are an essential tool in discrete mathematics and have applications in several areas including computational biology, image analysis, speech recognition, and information retrieval. Given any such metric D on a finite set X, an important problem is to find appropriate ways to realize D by weighting the edges in some graph G containing X in its vertex set such that D(x,y) equals the length of a shortest path from x to y in G for all x,y∈X. Here we focus on realizations with minimum total edge weight, called optimal realizations. By considering the 2-connected components and bridges in any optimal realization G of D we obtain an additive decomposition of D into simpler metrics. We show that this decomposition, called the block decomposition, is canonical in that it only depends on D and not on G, and that the decomposition can be computed in O(|X|3) time. As well as providing a fundamental new way to decompose any finite metric space, we expect that the block decomposition will provide a useful preprocessing tool for deriving metric realizations.

The Incentive Mechanism of the Subject Librarians with Computer Information Retrieval Under Competition

Based on the H-M model, the optimal incentive model under competition was constructed, the best incentive system was constructed by solving the model, and its incentive characteristics are analyzed. Based on the theory of econometrics, an empirical model of incentive system was constructed, and the correlation and significance between the main core variables and incentive intensity are empirically tested. The results show that competition has a significant effect and positive effect on incentive level. Incentive level has a non-linear correlation with service cost and risk preference, within a certain range, the best incentive level is negatively related to them.

Discriminative semi-supervised non-negative matrix factorization for data clustering

Recently semi-supervised non-negative matrix factorization (NMF) has received a lot of attentions in computer vision, information retrieval and pattern recognition, because that partial label information can produce considerable improvement in learning accuracy of the algorithms. However, the existing semi-supervised NMF algorithms cannot make full use of label information, that is, they cannot guarantee that the labeled data of different clusters will not be classified into a same group in the new representation space. In this paper, we propose a novel discriminative semi-supervised NMF (DSSNMF) algorithm, which utilizes the label information of a fraction of the data as a discriminative constraint. We explore the proposed DSSNMF method with two different cost function formulations and provide the corresponding update rules for the optimization problems. Empirical experiments demonstrate the effectiveness of our novel algorithm through a set of evaluations based on real-world applications.

Efficient Data Structures for Range Shortest Unique Substring Queries

Let T[1,n] be a string of length n and T[i,j] be the substring of T starting at position i and ending at position j. A substring T[i,j] of T is a repeat if it occurs more than once in T; otherwise, it is a unique substring of T. Repeats and unique substrings are of great interest in computational biology and information retrieval. Given string T as input, the Shortest Unique Substring problem is to find a shortest substring of T that does not occur elsewhere in T. In this paper, we introduce the range variant of this problem, which we call the Range Shortest Unique Substring problem. The task is to construct a data structure over T answering the following type of online queries efficiently. Given a range [α,β], return a shortest substring T[i,j] of T with exactly one occurrence in [α,β]. We present an O(nlogn)-word data structure with O(logwn) query time, where w=Ω(logn) is the word size. Our construction is based on a non-trivial reduction allowing for us to apply a recently introduced optimal geometric data structure [Chan et al., ICALP 2018]. Additionally, we present an O(n)-word data structure with O(nlogϵn) query time, where ϵ&gt;0 is an arbitrarily small constant. The latter data structure relies heavily on another geometric data structure [Nekrich and Navarro, SWAT 2012].

Research on the Application and Influence of Computer Information Retrieval Technology in Library and Information

From the beginning of computer information technology to now, the computer technology as the core of information retrieval technology has been widely used in various fields of life. At the same time, with the increasing demand of information users, the application of information retrieval technology in Library and information is gradually explored. It is also found that the application of computer technology in information retrieval can quickly and accurately search out all kinds of books and information needed by users[1]. This paper briefly analyzes the current situation of information retrieval, the evaluation of Library and information research, the application and advantages of information retrieval in Library and information. Finally, the corresponding conclusions are drawn.

Automatic extraction of subordinate clauses and its application in second language acquisition research

Clause subordination is an important linguistic phenomenon that is relevant to research in psycholinguistics, cognitive and behavioral sciences, language acquisition, and computational information retrieval. The paper presents a comprehensive tool called AutoSubClause, which is specifically designed for extracting subordinate clause (SC) information from natural English production. Using dependency parsing, AutoSubClause is able to extract not only information characterizing the three main types of SCs—complement, adverbial, and relative clauses—but also information regarding the internal structure of different clause types and their semantic and structural relations with elements of the main clause. Robustness testing of the system and its underlying dependency parser Stanford CoreNLP showed satisfactory results. To demonstrate the usefulness of AutoSubClause, we used it to analyze a large-scale learner corpus and investigate the effects of first language (L1) on the acquisition of subordination in second language (L2) English. Our analysis shows that learners from an L1 that is typologically different from the L2 in clause subordination tend to have different developmental trajectories from those whose L1 is typologically similar to the L2. Furthermore, the developmental patterns for different types of SCs also vary. This finding suggests the need to approach clausal subordination as a multi-componential construct rather than a unitary one, as is the case in most previous research. Finally, we demonstrate how NLP technology can support research questions that rely on linguistic analysis across various disciplines and help gain new insights with the increasing opportunities for up-scaled analysis.

Robust nonnegative matrix factorization with structure regularization

Nonnegative matrix factorization (NMF) has attracted more and more attention due to its wide applications in computer vision, information retrieval, and machine learning. In contrast to the original NMF and its variants, this paper proposes a novel unsupervised learning framework, called robust structured nonnegative matrix factorization (RSNMF) which respects both global and local structures of the data space. Specifically, to learn a discriminative representation, RSNMF explores both the global structure via considering the data variance and the local structure via exploiting the data neighborhood. To well address the problem of noise and outliers, it imposes joint L2,1-norm minimization on both the loss function of NMF and the regularization of the basis matrix. The geometric structure and the joint L2,1-norm are formulated as an optimization model, which is solved by the proposed iterative algorithm. Finally, the convergence of RSNMF is analyzed theoretically and empirically. The experimental results on real-world data sets show the effectiveness of our proposed algorithm in comparison to state-of-the-art algorithms.

Semantic association computation: a comprehensive survey

Semantic association computation is the process of quantifying the strength of a semantic connection between two textual units, based on different types of semantic relations. Semantic association computation is a key component of various applications belonging to a multitude of fields, such as computational linguistics, cognitive psychology, information retrieval and artificial intelligence. The field of semantic association computation has been studied for decades. The aim of this paper is to present a comprehensive survey of various approaches for computing semantic associations, categorized according to their underlying sources of background knowledge. Existing surveys on semantic computation have focused on a specific aspect of semantic associations, such as utilizing distributional semantics in association computation or types of spatial models of semantic associations. However, this paper has put a multitude of computational aspects and factors in one picture. This makes the article worth reading for those researchers who want to start off in the field of semantic associations computation. This paper introduces the fundamental elements of the association computation process, evaluation methodologies and pervasiveness of semantic measures in a variety of fields, relying on natural language semantics. Along the way, there is a detailed discussion on the main categories of background knowledge sources, classified as formal and informal knowledge sources, and the underlying design models, such as spatial, combinatorial and network models, that are used in the association computation process. The paper classifies existing approaches of semantic association computation into two broad categories, based on their utilization of background knowledge sources: knowledge-rich approaches; and knowledge-lean approaches. Each category is divided further into sub-categories, according to the type of underlying knowledge sources and design models of semantic association. A comparative analysis of strengths and limitations of various approaches belonging to each research stream is also presented. The paper concludes the survey by analyzing the pivotal factors that affect the performance of semantic association measures.

International Legal Scholarship and the Challenge of Digitalization

Abstract The paper argues that digitalization primarily presents a methodological challenge for international legal scholarship. Three developments are relevant in this context: the datafication of law, computerized information retrieval, and the differentiation of legal knowledge. International legal scholarship has benefited from treating legal texts, legal relationships as well as legal interactions and decision-making “as data”. Typically, quantitative methods used on this data include text mining, network analysis, cluster analysis, and regression analysis. While data-driven scholarship cannot replace a hermeneutic approach to international law, it is likely to change the dimensionality of legal research, require adaptations of the law school curriculum, and enhance the interdisciplinary connectivity of international legal scholarship.

Empirical evaluation of deep learning models for sentiment analysis

The availability of computing resources and generation of large scale data emanating from Artificial Intelligence, Internet of Things and social media platforms have resulted into resurgence of deep learning technology. Deep learning architectures have been successfully adopted to solve the problems arising in variety of domains such as computer vision, information retrieval, robotics, and natural language processing, etc. Due to inherent ability of deep architectures to extract hierarchical structures from complex multimedia data, they have been widely used for the tasks of classification, regression and prediction. Motivated by the same, this paper addresses the problem of identifying the subjective information from text documents and predicting the sentiments at sentence level using deep feedforward neural network with global average pooling and long short term memory model with dense layers. The experimentation details state that both models are on par and provide good accuracy on the benchmarked dataset of sentiment classification.

Modality-Invariant Image-Text Embedding for Image-Sentence Matching

Performing direct matching among different modalities (like image and text) can benefit many tasks in computer vision, multimedia, information retrieval, and information fusion. Most of existing works focus on class-level image-text matching, called cross-modal retrieval , which attempts to propose a uniform model for matching images with all types of texts, for example, tags, sentences, and articles (long texts). Although cross-model retrieval alleviates the heterogeneous gap among visual and textual information, it can provide only a rough correspondence between two modalities. In this article, we propose a more precise image-text embedding method, image-sentence matching, which can provide heterogeneous matching in the instance level. The key issue for image-text embedding is how to make the distributions of the two modalities consistent in the embedding space. To address this problem, some previous works on the cross-model retrieval task have attempted to pull close their distributions by employing adversarial learning. However, the effectiveness of adversarial learning on image-sentence matching has not been proved and there is still not an effective method. Inspired by previous works, we propose to learn a modality-invariant image-text embedding for image-sentence matching by involving adversarial learning. On top of the triplet loss--based baseline, we design a modality classification network with an adversarial loss, which classifies an embedding into either the image or text modality. In addition, the multi-stage training procedure is carefully designed so that the proposed network not only imposes the image-text similarity constraints by ground-truth labels, but also enforces the image and text embedding distributions to be similar by adversarial learning. Experiments on two public datasets (Flickr30k and MSCOCO) demonstrate that our method yields stable accuracy improvement over the baseline model and that our results compare favorably to the state-of-the-art methods.

Enabling Cooperative Privacy-preserving Personalized search in cloud environments

With the booming of the information society, individuals and companies have been generating huge amounts of data every second. As a result, as a new storage tool, cloud computing has provided great convenience for data storage and application. However, it is worth noting that security and privacy issues in cloud computing have hindered its further development. To solve this problem, researchers proposed to encrypt the sensitive data. However, the encrypted data brought some follow-up issues such as an increased computational overhead and information retrieval inconvenience. In this paper, to enable users to obtain the most satisfactory results in searching outsourced encrypted data and reduce the computational overhead of cloud servers as well, the Cooperative Privacy-preserving Personalized Search (CPPS) scheme in cloud environments is proposed, which makes use of matrix encryption to ensure the privacy of the user. Combining the search of multiple users has greatly reduced the computing cost of the cloud server and guaranteed the accuracy of the user’s personalized search results at the same time. Experiments on the Yelp dataset indicate that the CPPS scheme has low index construction overhead, low computational overhead and communication overhead while providing users with personalized search results.

Impact of response latency on sponsored search

Recent research in the human computer interaction and information retrieval areas has revealed that search response latency exhibits a clear impact on the user behavior in web search. Such impact is reflected both in users’ subjective perception of the usability of a search engine and in their interaction with the search engine in terms of the number of search results they engage with. However, a similar impact analysis has been missing so far in the context of sponsored search. Since the predominant business model for commercial search engines is advertising via sponsored search results (i.e., search advertisements), understanding how response latency influences the user interaction with the advertisements displayed on the search engine result pages is crucial to increase the revenue of a commercial search engine. To this end, we conduct a large-scale analysis using query logs obtained from a commercial web search. We analyze the short-term and long-term impact of search response latency on the querying and clicking behaviors of users using desktop and mobile devices to access the search engine, as well as the corresponding impact on the revenue of the search engine. This analysis demonstrates the importance of serving sponsored search results with low latency and provides insight into the ad serving policy of commercial search engines to ensure long-term user engagement and search revenue.

Graph regularized supervised cross-view hashing

Hashing methods have received significant attention for effective and efficient large scale similarity search in computer vision and information retrieval community. However, most existing cross-view hashing methods mainly focus on either similarity preservation of data or cross-view correlation. In this paper, we propose a graph regularized supervised cross-view hashing (GSCH) to preserve both the semantic correlation and the intra-view and inter view similarity simultaneously. In particular, GSCH uses intra-view similarity to estimate inter-view similarity structure. We further propose a sequential learning approach to derive the hashing function for each view. Experimental results on benchmark datasets against state-of-the-art methods show the effectiveness of our proposed method.