Specific Dictionary Research Articles

Corpus-based Lexicography for Lesser-resourced Languages — Maximizing the Limited Corpus

This article focuses on lesser-resourced languages for which only very limited corpora are available and how such relatively small and often unbalanced, raw corpora could be maximally utilized for lexicographic purposes to obtain similar results as for corpora. Sepedi and Afrikaans will be studied in this regard. The aim is to determine to what extent enlarging a corpus from e.g. one to 10 million, and from 10 million to 100 million words enhances its potential for (a) macrostructure compilation, (b) sourcing information on the most important microstructural aspects and (c) the creation of lexicographic tools. It will be argued that valuable and even sufficient data for the compilation of a specific dictionary can be extracted from a relatively small corpus of approximately one million words but that bigger in some instances indeed means better.

Street Poetry in Interaction with (Living) Language

In the 1970s a new musical genre called rap appeared in the United States, continuing the tradition of rock and punk music. In about twenty years, this new form of protest poetry created global sociolinguistic changes because its presence helped shape a special social group with a special lexicon and grammar. Rap uses both standard and colloquial vocabulary and syntax. Its traditional origin in poor black urban neighborhoods justifies the use of the term street poetry.

Robust face identification using DTCWT and PCA subspace based sparse representation

This paper presents a robust method for recognizing human faces with varying illumination as well as partial occlusion. In the proposed approach, a dual-tree complex wavelet transform (DTCWT) is employed to normalize the illumination variation in the logarithm domain. In order to minimize the variations under different lighting conditions, appropriate low frequency DTCWT subbands are truncated and the rest of the directional subbands are used to reconstruct the stable invariant face. Using the fundamental concept that patterns from a single object class lie in a linear subspace, we develop class specific dictionaries using principal component analysis (PCA) based subspace learning on illumination invariant faces. By representing the pre-processed probe image against each dictionary using l1 regularization into PCA reconstruction, target face and sparse noises are effectively factorized. Then, identification decision is made in favor of a class with minimum reconstruction error. Evaluations on challenging probe images demonstrate that the proposed method performs favorably against several state of the art methods.

Two Way Question Classification in Higher Education Domain

Question classification plays vital role in Question Answering (QA) systems. The task of classifying a question to appropriate class is performed to predict the question type of the natural language question. In this paper, initially we have presented a brief overview of classification approaches adapted by different question answering systems so far and then propose a two-way question classification approach for higher education domain which not only identifies focus word and question class but also reduces answer search space within corpus comprise of question-answer pair, adding to the classification accuracy. For precise semantic interpretation of domain keywords, a domain specific dictionary is constructed which primarily have four domain word type. Classified features are built upon domain attributes in the form of constraints. The experiment proved the efficiency for restricted domain, even though we used quite simplistic approach.

MegaMiner: A Tool for Lead Identification Through Text Mining Using Chemoinformatics Tools and Cloud Computing Environment.

Virtual screening is an indispensable tool to cope with the massive amount of data being tossed by the high throughput omics technologies. With the objective of enhancing the automation capability of virtual screening process a robust portal termed MegaMiner has been built using the cloud computing platform wherein the user submits a text query and directly accesses the proposed lead molecules along with their drug-like, lead-like and docking scores. Textual chemical structural data representation is fraught with ambiguity in the absence of a global identifier. We have used a combination of statistical models, chemical dictionary and regular expression for building a disease specific dictionary. To demonstrate the effectiveness of this approach, a case study on malaria has been carried out in the present work. MegaMiner offered superior results compared to other text mining search engines, as established by F score analysis. A single query term 'malaria' in the portlet led to retrieval of related PubMed records, protein classes, drug classes and 8000 scaffolds which were internally processed and filtered to suggest new molecules as potential anti-malarials. The results obtained were validated by docking the virtual molecules into relevant protein targets. It is hoped that MegaMiner will serve as an indispensable tool for not only identifying hidden relationships between various biological and chemical entities but also for building better corpus and ontologies.

Графическая иллюстрация как средство семантизации терминов в одноязычных толковых и специальных словарях

Графическая иллюстрация как средство семантизации терминов в одноязычных толковых и специальных словарях

Towards the ideal special language translation dictionary

In order to approach the ideal special language translation dictionary, a functional approach is preferred to a structural one. Functionality can be measured by means of the relevance principle, known from the theory of relevance. Through a relevance-based analysis of prototypical special language terminological dictionaries and general language translation dictionaries it is concluded that by taking over the relevant criteria from these, it should be possible to conceive the ideal special language translation dictionary.

Multiple Sparse Representations Classification.

Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this empirical study we propose to further leverage the redundancy of the learned dictionaries to achieve a more accurate classifier. In conventional SRC, each image pixel is associated with a small patch surrounding it. Using these patches, a dictionary is trained for each class in a supervised fashion. Commonly, redundant/overcomplete dictionaries are trained and image patches are sparsely represented by a linear combination of only a few of the dictionary elements. Given a set of trained dictionaries, a new patch is sparse coded using each of them, and subsequently assigned to the class whose dictionary yields the minimum residual energy. We propose a generalization of this scheme. The method, which we call multiple sparse representations classification (mSRC), is based on the observation that an overcomplete, class specific dictionary is capable of generating multiple accurate and independent estimates of a patch belonging to the class. So instead of finding a single sparse representation of a patch for each dictionary, we find multiple, and the corresponding residual energies provides an enhanced statistic which is used to improve classification. We demonstrate the efficacy of mSRC for three example applications: pixelwise classification of texture images, lumen segmentation in carotid artery magnetic resonance imaging (MRI), and bifurcation point detection in carotid artery MRI. We compare our method with conventional SRC, K-nearest neighbor, and support vector machine classifiers. The results show that mSRC outperforms SRC and the other reference methods. In addition, we present an extensive evaluation of the effect of the main mSRC parameters: patch size, dictionary size, and sparsity level.

Towards Understanding Egyptian Arabic Dialogues

Labelling of user's utterances to understanding his attends which called Dialogue Act (DA) classification, it is considered the key player for dialogue language understanding layer in automatic dialogue systems. In this paper, we proposed a novel approach to user's utterances labeling for Egyptian spontaneous dialogues and Instant Messages using Machine Learning (ML) approach without relying on any special lexicons, cues, or rules. Due to the lack of Egyptian dialect dialogue corpus, the system evaluated by multi-genre corpus includes 4725 utterances for three domains, which are collected and annotated manually from Egyptian call-centers. The system achieves F1 scores of 70. 36% overall domains.

WHETHER DOUBLETS-SYNONYMS ARE IDENTICAL OR NOT

The article considers pairs of lexical units, which in lexicology are defined as doublets or absolute synonyms. The main idea of the article is to show functional features of doublet units which would allow not to consider such units as absolutely identical. Based on the analysis of Russian nouns, noted in special publications and dictionaries as absolute synonyms, we show that there is often differentiation of elements of lexical pairs. It appears that many absolute synonyms do not disintegrate in the process of their functioning, as linguists predict (due to semantic and formal redundancy of such verbal forms), but many absolute synonyms do exist for a long time without any apparent transformation because there are factors (reasons) in the language that promote their functioning. The analysis shows that a set of factors supporting the existence of doublet forms is specific for the units of terminological character on the one hand, and for generally used absolute synonyms on the other hand.

English Vocabulary in Video Clips on Travel and Tourism

Information and communication technologies are an inseparable part of current university education. The authors have been teaching students of Management of Travel and Tourism at the University of Hradec Kralove, Czech Republic. The Management of Travel and Tourism students need to be aware of currently used specialized vocabulary in their field of study. Since 2010, their teachers have been using in their language classes video clips from the Internet. Watching video clips and studying the language used in them improves their command of currently used English for tourism. The purpose of this contribution is to inform about an upcoming study of the language used in video clips focusing on travel and tourism, whose transcripts will be analyzed by means of the British National Corpus. The method used is document analysis, more specifically word frequency analysis. So far, the authors have used about 80 English-language video clips on travel and tourism in their classes. The number of words used in them reaches about 40,000. The outcome of this research should be a list of the most frequently used words, phrases and idioms as they appear in the above mentioned video clips. The benefits of this research should include the basis for teaching travel and tourism vocabulary, grammar structures, or stylistic variations. Another outcome will be a specialized dictionary of professional collocations and terms in the field of travel and tourism.

Structured sparse priors for image classification.

Model-based compressive sensing (CS) exploits the structure inherent in sparse signals for the design of better signal recovery algorithms. This information about structure is often captured in the form of a prior on the sparse coefficients, with the Laplacian being the most common such choice (leading to l1 -norm minimization). Recent work has exploited the discriminative capability of sparse representations for image classification by employing class-specific dictionaries in the CS framework. Our contribution is a logical extension of these ideas into structured sparsity for classification. We introduce the notion of discriminative class-specific priors in conjunction with class specific dictionaries, specifically the spike-and-slab prior widely applied in Bayesian sparse regression. Significantly, the proposed framework takes the burden off the demand for abundant training image samples necessary for the success of sparsity-based classification schemes. We demonstrate this practical benefit of our approach in important applications, such as face recognition and object categorization.

Texts sentiment-analysis application for public opinion assessment

The paper describes an approach to the emotional tonality assessment of natural language texts based on special dictionaries. A method for an automatic assessment of public opinion by means of sentiment-analysis of reviews and discussions followed by published Web-documents is proposed. The method is based on statistics of words in the documents. A pilot model of the software system implementing the sentiment-analysis of natural language text in Russian based on a linear assessment scale is developed. A syntactic analysis and words lemmatization are used to identify terms more correctly. Tonality dictionaries are presented in editable format and are open for enhancing. The program system implementing a sentiment-analysis of the Russian texts based on open dictionaries of tonality is presented for the first time.

Development of Web Learning Support System using “My Dictionary” in English Study

Abstract Although powerful, Web learning is considered less effective for non-aggressive learners. By introducing “Examine learning”, “Cooperative learning”, and “Gamification”, learners can create “My dictionary”, refer to others’ dictionaries, and write and evaluate comments. Using the information in “My dictionary”, like the number of registered words in “My dictionary” and comments written in others’ dictionaries and the evaluated points from others, the learners gain points as part of a cooperative game. Through these methods, we attempted to compensate for Web learning's weaknesses. Consequently, the learner develops their specific dictionary and referring to the dictionary and evaluating others’ dictionaries, continues learning easily.

Językowy obraz wieśniaka we współczesnej polszczyźnie

The article presents linguistic image of a villager in the contemporary Polish language. The study was conducted basing on definitions in general and specialized dictionaries of the Polish language as well as questionnaires. The analysis allowed to describe the stereotype of a villager in the consciousness of Poles, which is based upon a combination of grammatical and metaphorical meanings. Even in lexicographical studies there was a disturbing lack of separation of the colloquial understanding of the term – wieśniak (a villager, a peasant) – an ill-mannered, badly behaved man. In most lexicographical studies the grammatical component "man from the village" was built in the definitions and metaphorical understanding of the lexeme. The questionnaires conducted confirm this thesis. For respondents a villager is a resident of the village, characterized by: a lack of sophistication, cultural ignorance, use of dialect, vulgarity, lack of intelligence. The results are surprising, considering that half of the respondents live in the countryside and 99% have at least weekly contact with someone living in rural areas.

He Interconnection and Interrelation Features of Words and Their Importance in Compiling Second Language Dictionaries

In the process of teaching language the most important task is to know the peculiarities of interconnection of words. And one of the effective factors of learning and teaching Uzbek as a second language is to conduct specific research on interconnection and interrelation features of words and to compose special dictionaries of the Uzbek language for Russian-speaking students. Meanwhile, the issue of interconnection and interrelation peculiarities of words has been investigated not only by many linguists but by some psychologists as well.One of those prominent psychological scientists is N.I.Jinkinn. According to him, the integration of speech units is divided into two phases. He also relates “the grammar field” with the second phase of speech-unitintegration. Furthermore, he points out that in the first phase of this process, words and word forms are spread out in the “speech field”. In the second phase a speaker selects the necessary words for the primary explanation of the idea,which is followed by the correct grammar form, and then collocates with a suitable word [1,45]. For instance, according to N.I.Jinkinn,a noun, an adjective and an adverb do not usually collocate with other words. They are called as lexics, which are spread in the “vocabulary field” but divided into characteristic groups. Keywords: Uzbek language, dictionaries, interrelation, second language, interconnection

Reconstructible Nonlinear Dimensionality Reduction via Joint Dictionary Learning.

This paper presents a parametric low-dimensional (LD) representation learning method that allows to reconstruct high-dimensional (HD) input vectors in an unsupervised manner. Under the assumption that the HD data and its LD representation share the same or similar local sparse structure, the proposed method achieves reconstructible dimensionality reduction via jointly learning dictionaries in both the original HD data space and its LD representation space. By regarding the sparse representation as a smooth function with respect to a specific dictionary, we construct an encoding-decoding block for learning LD representations from sparse coefficients of HD data. It is expected that this learning process preserves the desirable structure of HD data in the LD representation space, and simultaneously allows a reliable reconstruction from the LD space back to the original HD space. In addition, the proposed single layer encoding-decoding block can be easily extended to deep learning structures. Numerical experiments on both synthetic data sets and real images show that the proposed method achieves strongly competitive and robust performance in data DR, reconstruction, and synthesis, even on heavily corrupted data. The proposed method can be used as an alternative approach to compressive sensing (CS); however, it can outperform the traditional CS methods in: 1) task-driven learning problems, such as 2-D/3-D data visualization, and 2) data reconstruction at a lower dimensional space.

Application of Content Specific Dictionaries in Still Image Coding

Application of Content Specific Dictionaries in Still Image Coding

Towards a supervised rescoring system for unstructured data bases used to build specialized dictionaries

<p align="justify">This article proposes the architecture for a system that uses previously learned weights to sort query results from unstructured data bases when building specialized dictionaries. A common resource in the construction of dictionaries, unstructured data bases have been especially useful in providing information about lexical items frequencies and examples in use. However, when building specialized dictionaries, whose selection of lexical items does not rely on frequency, the use of these data bases gets restricted to a simple provider of examples. Even in this task, the information unstructured data bases provide may not be very useful when looking for specialized uses of lexical items with various meanings and very long lists of results. In the face of this problem, long lists of hits can be rescored based on a supervised learning model that relies on previously helpful results. The allocation of a vast set of high quality training data for this rescoring system is reported here. Finally, the architecture of sucha system,an unprecedented tool in specialized lexicography, is proposed.</p>

Transparency and Translatability of the Terminological Metaphor in the Domain of Computer Science (A Contrastive Analysis)

Abstract Our research, Transparency and translatability of the terminological metaphor in the domain of the internet, is a contrastive analysis in the topic of the metaphor, especially. The relationship between the common and the special lexicon in the domain of the Internet in the English language as source language, the relationship between the common denominator between the source language and the semantic basis, of equivalence in the target language represent the aims of the research. The languages in which the analysis is carried out are different from the genealogical and typological point of view (the English language on the one hand, the Romance language and Hungarian on the other). The perspective is a descriptive-semasiological one, and the methods applied - the paradigmatic and syntagmatic analysis, the contrastive analysis - are adapted to this perspective. The transparency in the meaning, the degree of translatability, the motivated character of the terminological metaphor, the role of linguistics / of semantics in the terminology of the Internet are only some of the conclusions of the research.