Dictionary Classification Research Articles

Класифікації електронних словників

The article deals with the problem of a typology of electronic dictionaries. The purpose of the article is to analyze modern approaches to determine the criteria for the classification of electronic lexicographic resources. Such methods as an analysis of scientific sources, comparison, generalization and systematization have been applied in the research. Based on the analysis of scientific literature, it was found that among researchers there is no single point on the criteria for classifying electronic dictionaries, which are a relatively new phenomenon on the market of lexicographic products and have evolved from machine-readable lexicons, exact copies of paper editions to complex digital lexicographic systems with a powerful arsenal of functions for several decades. In general, there are two common approaches to the classification of dictionaries among scholars. Some researchers (N. Kameneva, А. Baranov et al. ) believe that the same classification criteria should be applied to electronic dictionaries as to traditional dictionaries, others ( V. Shyrokov, L. Belyaeva, R. Mysak, А . Lehr , І . Sazhenin et al. ) believe that the accepted division of traditional dictionaries is not relevant to computer dictionaries and lexicographic databases. Electronic dictionaries are often classified according to criteria such as form, location, purpose, linguistic characteristics, printed prototype, interactivity, the presence of multimedia objects, and so on. And they are classified according to specific technical criteria: minimum hardware requirements, development environment, software, installation, method of execution and download, the number of dictionary databases, the amount of random access memory, the ability to expand the vocabulary, conversion dictionary databases in text format, etc. It has been established that modern electronic dictionaries tend to hybridize, often combining alphabetical and thematic methods of material organization, lexical and encyclopedic information, which complicates the possibility of distinguishing clear differential features of a particular lexicographic resource and its rigid, strict classification. The multiplicity of criteria for comparing dictionaries complicates the construction of a single general classification of dictionaries of different types, and multifaceted characterization of a particular dictionary can be carried out by the facet method using a basic list of parameters, which remains open and can be supplemented with the development of lexicographic resources system.

Synthetic Network and Search Filter Algorithm in English Oral Duplicate Correction Map

Combining the communicative language competence model and the perspective of multimodal research, this research proposes a research framework for oral communicative competence under the multimodal perspective. This not only truly reflects the language communicative competence but also fully embodies the various contents required for assessment in the basic attributes of spoken language. Aiming at the feature sparseness of the user evaluation matrix, this paper proposes a feature weight assignment algorithm based on the English spoken category keyword dictionary and user search records. The algorithm is mainly based on the self-built English oral category classification dictionary and converts the user’s query vector into a user-English-speaking type vector. Through the calculation rules proposed in this paper, the target user’s preference score for a specific type of spoken English is obtained, and this score is assigned to the unrated item of the original user’s feature matrix as the initial starting score. At the same time, in order to solve the problem of insufficient user similarity calculation accuracy, a user similarity calculation algorithm based on “Synonyms Cilin Extended Edition” and search records is proposed. The algorithm introduces “Synonyms Cilin” to calculate the correlation between the semantic items, vocabulary, and query vector in the user query record to obtain the similarity between users and finally gives a user similarity calculation that integrates user ratings and query vectors method. For the task of Chinese grammar error correction, this article uses two methods of predicting the relationship between words in the corpus, Word2Vec and GloVe, to train the word vectors of different dimensions and use the word vectors to represent the text features of the experimental samples, avoiding sentences brought by word segmentation. On the basis of word vectors, the advantages and disadvantages of CNN, LSTM, and SVM models in this shared task are analyzed through experimental data. The comparative experiment shows that the method in this paper has achieved relatively good results.

Auto-encoder based structured dictionary learning for visual classification

Dictionary learning and deep learning can be combined to boost the performance of classification tasks. However, existing combined methods often learn multi-level dictionaries each of which is embedded in a network layer, involve a large number of parameters (elements of many dictionaries) and thus easily result in prohibitive computational cost and even overfitting. In this paper, we present a novel deep Auto-Encoder based Structured Dictionary (AESD) learning model, where we need to learn only one dictionary which is composed of class-specific sub-dictionaries, and supervision is introduced by imposing discriminative category constraints to empower the dictionary with discrimination. The encoding layers are designed with shared parameters which are exactly dependent on the dictionary carried by the decoding layer. This characterizes the learning process by forward-propagation based optimization w.r.t the dictionary only, leading to a light-weight network training. In addition to utilizing directly the trained encoding network combined with a minimum-reconstruction-residual scheme for single image based classification, to expand the application spectrum of our method, in the testing phase, we extend the proposed prototype into a Convolutional Encoder based Block Sparse Representation (CEBSR) model to promote the latent block sparsity in the joint representation of an image set, achieving improved image set based classification. Extensive experiments verify the performance of the learned dictionary for image classification, and the superiority of our extended model over the state-of-the-art image set classification methods.

Classification of dictionaries of terms in special lexicography

The article is devoted to one of the recent problems of terminography, which studies terminological dictionaries, deals with the development of principles and bases how to comply terminological lexicons of different types, concerns with their classification, as well as arrangement and unification of national terminology. The problem of classification of terminographic editions is of great importance: it constantly attracts meticulous attention of Ukrainian and foreign linguists, however nowadays there is no any unified set of features for distinguishing dictionaries. Is was specified that modern terminological lexicography requires a classified description of terminological dictionaries of different types. Linguists create typologies, classifications, as well as specific and typological classifications of dictionaries. Schematically, the hierarchy of subordination of special editions can be displayed as it follows: linguistic dictionaries (type) → system dictionaries (subtype) → professional dictionaries (class) → terminological dictionaries (kind) → … (variable kinds). The meaning of the term „typology” is broader than the essence of the concept „classification”. Correspondingly, it is proposed to use the term classification of terminological dictionaries to denote an arranged description of terminographic editions with their division into types, subtypes, classes, and subclasses according to common features and purpose. It is found out that examined reliable typologies and classifications of terminological dictionaries are structured as taxonomic systems based on pragmatic features. They determine the special purpose of dictionaries, divide them according to structural, functional and other features, and establish characteristic features of mega-, macro-, and microstructure of editions. The researched typologies and classifications are not complete, as they focus only on available terminological dictionaries. The necessity of terminological edition classification was proved; it should be noted that possible perspective of variable kinds of special editions for the needs of time and science should be taked into the account, in particular, the “dictionary of the future” – a terminological dictionary of a combined type.

ЖЕСТЫ И РЕЧЬ В НОВОМ ЛИНГВО-СЕМИОТИЧЕСКОМ СЛОВАРЕ

In oral communication, an active role belongs not only to verbal units but to nonverbal units as well. These units constitute the lexical part of the so called body language and are known by name of gestures (of different semiotic classes). The paper presents the New linguo-semiotic dictionary aimed to solve some lexical aspects of the problem of multimodality the kernel of which is the description of general patterns and formulation of specific rules of speech-gesture interaction. It is shown that this dictionary takes a separate taxon in a classification of dictionaries The paper reveals the ideology, structure and content of the linguo-semiotic dictionary. Its basis is the notion of semiotic conceptualization of human body. In the first part of the paper the main constituents of the conceptualization, tools and method of lexicographic analysis and metalanguage of the dictionary are described. The second part contains two examples of dictionary entries (ЗУБЫ ‘teeth’ and НОГТИ ‘nails’) and it also demonstrates the links between several types of information within the vocable.

Терминография языка саха: формирование и становление

The paper, following the modern theory of terminography, considers the formation and composition of the terminology of the Sakha language selection and quality of vocabulary dictionaries, and their composition (inventory and interpretation) parameters. Yakut terminography has dictionaries for all branches of knowledge: nine - in natural sciences, six - in social (humanitarian) sciences, one - in technical sciences, with there being also four macro-compositional (multi-field) dictionaries. The authors provide a classification analysis of terminological dictionaries for typologies and compositional parameters. According to compositional parameters, there are multi-field and single-field dictionaries in Sakha terminology. An inventory of terms is a collection and description of all words-terms belonging to the selected field of knowledge or its thematic fragment. Inventory dictionaries are Russian-Sakha, once again confirming the leading role of the Russian terminological school. New concepts with their designations come to us through the Russian language. The defining type includes dictionaries with the head vocabulary provided with definitions (brief interpretations). The definition should be accurate, concise, but sufficient and should not contain redundant information. The definition is an excellent technique for highlighting the terminological meaning of a polysemantic word and a word with an undifferentiated meaning. With terminological dictionaries intersecting in some way, the faceted principle of dividing them was subsequently adopted. Faceted typology is convenient for sorting varieties of dictionaries by their parallel bases. Sakha terminography, comprising multi-field and mixed dictionaries, needs a faceted typology. Faceted typology involves the identification of specific lexicographic parameters serving as the basis for the classification of dictionaries.

A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences.

Nasopharyngeal carcinoma (NPC) is the most common malignant tumor of the nasopharynx. The delicate nature of the nasopharyngeal structures means that noninvasive magnetic resonance imaging (MRI) is the preferred diagnostic technique for NPC. However, NPC is a typically infiltrative tumor, usually with a small volume, and thus, it remains challenging to discriminate it from tightly connected surrounding tissues. To address this issue, this study proposes a voxel-wise discriminate method for locating and segmenting NPC from normal tissues in MRI sequences. The located NPC is refined to obtain its accurate segmentation results by an original multiviewed collaborative dictionary classification (CODL) model. The proposed CODL reconstructs a latent intact space and equips it with discriminative power for the collective multiview analysis task. Experiments on synthetic data demonstrate that CODL is capable of finding a discriminative space for multiview orthogonal data. We then evaluated the method on real NPC. Experimental results show that CODL could accurately discriminate and localize NPCs of different volumes. This method achieved superior performances in segmenting NPC compared with benchmark methods. Robust segmentation results show that CODL can effectively assist clinicians in locating NPC.

Learning efficient structured dictionary for image classification

Recent years have witnessed the success of dictionary learning (DL) based approaches in the domain of pattern classification. In this paper, we present an efficient structured dictionary learning (ESDL) method which takes both the diversity and label information of training samples into account. Specifically, ESDL introduces alternative training samples into the process of dictionary learning. To increase the discriminative capability of representation coefficients for classification, an ideal regularization term is incorporated into the objective function of ESDL. Moreover, in contrast with conventional DL approaches which impose computationally expensive L1-norm constraint on the coefficient matrix, ESDL employs L2-norm regularization term. Experimental results on benchmark databases (including four face databases and one scene dataset) demonstrate that ESDL outperforms previous DL approaches. More importantly, ESDL can be applied in a wide range of pattern classification tasks.

Some approaches to classification of dictionaries in lexicography

Some approaches to classification of dictionaries in lexicography

Comparative Review of 30 Day Non-Relapse Mortality (NRM) in B-Cell Lymphomas Associated with Anti-CD19 Chimeric Antigen Receptor T-Cells (CAR-T) from FDA Database, Clinical Studies, and MD Anderson

Comparative Review of 30 Day Non-Relapse Mortality (NRM) in B-Cell Lymphomas Associated with Anti-CD19 Chimeric Antigen Receptor T-Cells (CAR-T) from FDA Database, Clinical Studies, and MD Anderson

Adaptive Learning Emotion Identification Method of Short Texts for Online Medical Knowledge Sharing Community.

The medical knowledge sharing community provides users with an open platform for accessing medical resources and sharing medical knowledge, treatment experience, and emotions. Compared with the recipients of general commodities, the recipients in the medical knowledge sharing community pay more attention to the intensity or overall evaluation of emotional vocabularies in the comments, such as treatment effects, prices, service attitudes, and other aspects. Therefore, the overall evaluation is not a key factor in medical service comments, but the semantics of the emotional polarity is the key to affect recipients of the medical information. In this paper, we propose an adaptive learning emotion identification method (ALEIM) based on mutual information feature weight, which captures the correlation and redundancy of features. In order to evaluate the proposed method's effectiveness, we use four basic corpus libraries crawled from the Haodf's online platform and employ Taiwan University NTUSD Simplified Chinese Emotion Dictionary for emotion classification. The experimental results show that our proposed ALEIM method has a better performance for the identification of the low-frequency words' redundant features in comments of the online medical knowledge sharing community.

Super-Resolution of Brain MRI Images Using Overcomplete Dictionaries and Nonlocal Similarity

Recently, the Magnetic Resonance Imaging (MRI) images have limited and unsatisfactory resolutions due to various constraints such as physical, technological and economic considerations. Super-resolution techniques can obtain high-resolution MRI images. The traditional methods obtained the resolution enhancement of brain MRI by interpolations, affecting the accuracy of the following diagnose process. The requirement for brain image quality is fast increasing. In this paper, we propose an image super-resolution (SR) method based on overcomplete dictionaries and inherent similarity of an image to recover the high-resolution (HR) image from a single low-resolution (LR) image. We explore the nonlocal similarity of the image to tentatively search for similar blocks in the whole image and present a joint reconstruction method based on compressive sensing (CS) and similarity constraints. The sparsity and self-similarity of the image blocks are taken as the constraints. The proposed method is summarized in the following steps. First, a dictionary classification method based on the measurement domain is presented. The image blocks are classified into smooth, texture and edge parts by analyzing their features in the measurement domain. Then, the corresponding dictionaries are trained using the classified image blocks. Equally important, in the reconstruction part, we use the CS reconstruction method to recover the HR brain MRI image, considering both nonlocal similarity and the sparsity of an image as the constraints. This method performs better both visually and quantitatively than some existing methods.

An Effective Dictionary Learning Algorithm Based on fMRI Data for Mobile Medical Disease Analysis

With the continuous development and progress of the healthcare monitoring system, medical diagnosis for human health plays a, particularly, critical role, which can help doctors make correct choices and effective treatment plans. However, effective feature extraction is very important for the analysis of functional magnetic resonance imaging data; the traditional feature-based dictionary learning algorithm ignores the relationship between atoms and the input samples, and the small sample data is prone to over-fitting. In this paper, we propose a new weighting mechanism, which effectively considers the relationship between the atom and the input sample; meanwhile, the cross-validation method performed well on obtaining additional validation sets but proved to be over-fitting on small datasets in the traditional dictionary learning algorithm. Therefore, $\mathrm {\ell 2}$ -norm and $\mathfrak{F}$ -norm regularization constraint is adopted to avoid over-fitting, achieve the limitations of the model space, and improve the generalization ability of the model. In order to extract features better, this paper uses the cosine similarity method to select good feature subsets, which effectively improves further the generalization ability and enhances the feature extraction accuracy. The results show that the improved dictionary classification algorithm has better performance in terms of accuracy, sensitivity, and specificity, and it also demonstrates that the proposed algorithm has an effective classification about mobile multimedia medical diseases, which can provide a better guidance for the diagnosis of later diseases, so as to promote the rapid development of medical feature extraction.

Exploiting class-wise coding coefficients: Learning a discriminative dictionary for pattern classification

Abstract Over the past decade, discriminative dictionary learning (DDL) has demonstrated the great success in various pattern classification problems. However, in previous DDL methods, the scheme that how to generate the effective coding coefficients for classification has not been well addressed. This paper proposes a novel DDL method, named CW-DDL, to learn a discriminative dictionary for classification by exploiting class-wise coding coefficients. In the proposed method, a label-aware constraint is first presented to make the coefficient matrix has class-wise approximate sparse structure. Then the graph regularization is further enforced on coding coefficients by utilizing the locality information of dictionary atoms. These two constrained terms reinforce each other in the learning process, resulting in a very robust and discriminative dictionary. Moreover, to obtain class-wise separation of coefficient vectors from different classes, a support vector based classifier is integrated into the objective function. Finally, an iterative algorithm is devised to solve the proposed method efficiently. Experimental results illustrate that the optimal coding coefficients derived by CW-DDL are very effective for pattern classification. The proposed method shows the superior performance to related DDL methods on several benchmark datasets, and coupled with the CNN features, it also leads to the state-of-art performance on the more challenging dataset.

Descriptor extraction based on a multilayer dictionary architecture for classification of natural images

This paper presents a descriptor extraction method in the context of image classification, based on a multilayer structure of dictionaries. We propose to learn an architecture of discriminative dictionaries for classification in a supervised framework using a patch-level approach. This method combines many layers of sparse coding and pooling in order to reduce the dimension of the problem. The supervised learning of dictionary atoms allows them to be specialized for a classification task. The method has been tested on known datasets of natural images such as MNIST, CIFAR-10 and STL, in various conditions, especially when the size of the training set is limited, and in a transfer learning application. The results are also compared with those obtained with Convolutional Neural Network (CNN) of similar complexity in terms of number of layers and processing pipeline.

Complexity-Based Discrepancy Measures Applied to Detection of Apnea-Hypopnea Events

In recent years, an increasing interest in the development of discriminative methods based on sparse representations with discrete dictionaries for signal classification has been observed. It is still unclear, however, what is the most appropriate way for introducing discriminative information into the sparse representation problem. It is also unknown which is the best discrepancy measure for classification purposes. In the context of feature selection problems, several complexity-based measures have been proposed. The main objective of this work is to explore a method that uses such measures for constructing discriminative subdictionaries for detecting apnea-hypopnea events using pulse oximetry signals. Besides traditional discrepancy measures, we study a simple one called Difference of Conditional Activation Frequency (DCAF). We additionally explore the combined effect of overcompleteness and redundancy of the dictionary as well as the sparsity level of the representation. Results show that complexity-based measures are capable of adequately pointing out discriminative atoms. Particularly, DCAF yields competitive averaged detection accuracy rates of 72.57% at low computational cost. Additionally, ROC curve analyses show averaged diagnostic sensitivity and specificity of 81.88% and 87.32%, respectively. This shows that discriminative subdictionary construction methods for sparse representations of pulse oximetry signals constitute a valuable tool for apnea-hypopnea screening.

Learning a discriminative dictionary for classification with outliers

Abstract Dictionary learning aims to find a dictionary where signals in some ensemble have sparse representations, and has been successfully applied for classification. However, traditional dictionary learning methods for classification assume there is no outlier in the training data, which may not be the case in practical applications. In this paper, we propose a new discriminative dictionary learning framework for classification, which simultaneously learns a discriminative dictionary and detects outliers in the data. The dictionary learning framework is formulated into an optimization problem with designed regularizers to promote both the discrimination and outlier-detection capability. We demonstrate the superior performance of the proposed approach in comparison with state-of-the-art alternatives by conducting extensive experiments on various image classification tasks.

SlangSD: building, expanding and using a sentiment dictionary of slang words for short-text sentiment classification

Sentiment information about social media posts is increasingly considered an important resource for customer segmentation, market understanding, and tackling other socio-economic issues. However, sentiment in social media is difficult to measure since user-generated content is usually short and informal. Although many traditional sentiment analysis methods have been proposed, identifying slang sentiment words remains a challenging task for practitioners. Though some slang words are available in existing sentiment lexicons, with new slang being generated with emerging memes, a dedicated lexicon will be useful for researchers and practitioners. To this end, we propose to build a slang sentiment dictionary to aid sentiment analysis. It is laborious and time-consuming to collect a comprehensive list of slang words and label the sentiment polarity. We present an approach to leverage web resources to construct a Slang Sentiment Dictionary (SlangSD) that is easy to expand. SlangSD is publicly available for research purposes. We empirically show the advantages of using SlangSD, the newly-built slang sentiment word dictionary for sentiment classification, and provide examples demonstrating its ease of use with a sentiment analysis system.

Super-resolution via supervised classification and independent dictionary training

Super-resolution (SR) reconstruction plays an important role in recovering the image details and improving the visual perception. In this paper, we propose a new and effective method based on the idea of classification reconstruction and independent dictionary training. Firstly, we extract some geometric features of images and design a new supervised classification method, which uses the decision tree to guarantee a better classification result. Secondly, the coefficients of the high-resolution (HR) and low-resolution (LR) patches are not equal strictly in fact, which enlighten us to train the HR and LR dictionaries independently. And then mapping matrices are learned to map LR coefficients into HR coefficients, which can not only help us improve reconstruction quality, but also just perform sparse coding one time in the reconstruction stage. At last, we enforce a global optimization on the initial reconstruction HR image based on the non-local means and the auto-regressive model. The experiments show that the method we proposed works better than other classic state-of-the-art methods.

Multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement

For the problems of missing details and performance of the colorization based on sparse representation, we propose a conceptual model framework for colorizing gray-scale images, and then a multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement (CEMDC) is proposed based on this framework. The algorithm can achieve a natural colorized effect for a gray-scale image, and it is consistent with the human vision. First, the algorithm establishes a multi-sparse dictionary classification colorization model. Then, to improve the accuracy rate of the classification, the corresponding local constraint algorithm is proposed. Finally, we propose a detail enhancement based on Laplacian Pyramid, which is effective in solving the problem of missing details and improving the speed of image colorization. In addition, the algorithm not only realizes the colorization of the visual gray-scale image, but also can be applied to the other areas, such as color transfer between color images, colorizing gray fusion images, and infrared images.