Search results for: word semantic

Authors: John Lorenzo Bautista, Yoon-Joong Kim

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In this paper, a set of Filipino Phonetically Balanced Word list consisting of 250 words (PBW250) were constructed for a phoneme-level ASR system for the Filipino language. The Entropy Maximization is used to obtain phonological balance in the list. Entropy of phonemes in a word is maximized, providing an optimal balance in each word’s phonological distribution using the Add-Delete Method (PBW algorithm) and is compared to the modified PBW algorithm implemented in a dynamic algorithm approach to obtain optimization. The gained entropy score of 4.2791 and 4.2902 for the PBW and modified algorithm respectively. The PBW250 was recorded by 40 respondents, each with 2 sets data. Recordings from 30 respondents were trained to produce an acoustic model that were tested using recordings from 10 respondents using the HMM Toolkit (HTK). The results of test gave the maximum accuracy rate of 97.77% for a speaker dependent test and 89.36% for a speaker independent test.

Keywords: entropy maximization, Filipino language, Hidden Markov Model, phonetically balanced words, speech recognition

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Authors: C. E. Laburu, M. A. Barros, A. F. Zompero, O. H. M. Silva

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The work presents a teaching strategy that employs syntagmatic and paradigmatic linguistic relations in order to monitor the understanding of physics students’ concepts. Syntagmatic and paradigmatic relations are theoretical elements of semiotics studies and our research circumstances and justified them within the research program of multi-modal representations. Among the multi-modal representations to learning scientific knowledge, the scope of action of syntagmatic and paradigmatic relations belongs to the discursive writing form. The use of such relations has the purpose to seek innovate didactic work with discourse representation in the write form before translate to another different representational form. The research was conducted with a sample of first year high school students. The students were asked to produce syntagmatic and paradigmatic of Newton’ first law statement. This statement was delivered in paper for each student that should individually write the relations. The student’s records were collected for analysis. It was possible observed in one student used here as example that their monemes replaced and rearrangements produced by, respectively, syntagmatic and paradigmatic relations, kept the original meaning of the law. In paradigmatic production he specified relevant significant units of the linguistic signs, the monemas, which constitute the first articulation and each word substituted kept equivalence to the original meaning of original monema. Also, it was noted a number of diverse and many monemas were chosen, with balanced combination of grammatical (grammatical monema is what changes the meaning of a word, in certain positions of the syntagma, along with a relatively small number of other monemes. It is the smallest linguistic unit that has grammatical meaning) and lexical (lexical monema is what belongs to unlimited inventories; is the monema endowed with lexical meaning) monemas. In syntagmatic production, monemas ordinations were syntactically coherent, being linked with semantic conservation and preserved number. In general, the results showed that the written representation mode based on linguistic relations paradigmatic and syntagmatic qualifies itself to be used in the classroom as a potential identifier and accompanist of meanings acquired from students in the process of scientific inquiry.

Keywords: semiotics, language, high school, physics teaching

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Authors: M. Hijazi, A. Zeki, A. Ismail

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An enormous amount of valuable human knowledge is preserved in documents. The rapid growth in the number of machine-readable documents for public or private access requires the use of automatic text classification. Text classification can be defined as assigning or structuring documents into a defined set of classes known in advance. Arabic text classification methods have emerged as a natural result of the existence of a massive amount of varied textual information written in the Arabic language on the web. This paper presents a review on the published researches of Arabic Text Classification using classical data representation, Bag of words (BoW), and using conceptual data representation based on semantic resources such as Arabic WordNet and Wikipedia.

Keywords: Arabic text classification, Arabic WordNet, bag of words, conceptual representation, semantic relations

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Authors: Ngan Nguyen

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The study analyses adjectives collocating with the word ‘education’ in the American language of the Corpus of Global Web-based English using a combination of corpus linguistic and discourse analytical methods to examine not only language patterns but also social political ideologies around the topic. Significant conclusions are deduced: (1) there are a large number of adjectival collocates of the word education which have been identified and classified into four categories representing four different aspects of education: level, quality, forms and types of education; (2) education, as in combination with three first categories, carries the meaning as the act and process of teaching and learning while with the last category having the meaning of a particular kind of teaching or training; (3) higher education is the topic that gains most concerns from the American public; (4) five most significant ideologies are discovered from the corpus: higher education associates with financial affairs, higher education is an industry, monetary policy of the government on higher education, people require greater accessibility to higher education and people value higher education. The study contributes to the field of developing meanings of words through corpus analysis and the field of discourse analysis.

Keywords: adjectival collocation, American context, corpus linguistics, discourse analysis, education

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Authors: S. B. Rathna Kumar, Sandya K. Varudhini, Aparna Ravichandran

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Telugu is a south central Dravidian language spoken in Andhra Pradesh, a southern state of India. The available speech identification tests in Telugu have been developed to determine the communication problems of individuals having a flat frequency hearing loss. These conventional speech audiometric tests would provide redundant information when used on individuals with high-frequency sloping hearing loss because of better hearing sensitivity in the low- and mid-frequency regions. Hence, conventional speech identification tests do not indicate the true nature of the communication problem of individuals with high-frequency sloping hearing loss. It is highly possible that a person with a high-frequency sloping hearing loss may get maximum scores if conventional speech identification tests are used. Hence, there is a need to develop speech identification test materials that are specifically designed to assess the speech identification performance of individuals with high-frequency sloping hearing loss. The present study aimed to develop speech identification test for individuals with high-frequency sloping hearing loss in Telugu. Individuals with high-frequency sloping hearing loss have difficulty in perception of voiceless consonants whose spectral energy is above 1000 Hz. Hence, the word lists constructed with phonemes having mid- and high-frequency spectral energy will estimate speech identification performance better for such individuals. The phonemes /k/, /g/, /c/, /ṭ/ /t/, /p/, /s/, /ś/, /ṣ/ and /h/are preferred for the construction of words as these phonemes have spectral energy distributed in the frequencies above 1000 KHz predominantly. The present study developed two word lists in Telugu (each word list contained 25 words) for evaluating speech identification performance of individuals with high-frequency sloping hearing loss. The performance of individuals with high-frequency sloping hearing loss was evaluated using both conventional and high-frequency word lists under recorded voice condition. The results revealed that the developed word lists were found to be more sensitive in identifying the true nature of the communication problem of individuals with high-frequency sloping hearing loss.

Keywords: speech identification test, high-frequency sloping hearing loss, recorded voice condition, Telugu

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Authors: Deepa Vijay, Chellammal Surianarayanan, Gopinath Ganapathy

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Requirements of a software project serve as a guideline for the entire project team which enable the team towards producing the right outcome. As requirements are the key in deciding the success of the project, it should be specified in an unambiguous manner. Also, the requirements should be complete and consistent. It should be interpreted in the same way by the entire software project team as the customer interprets. Specifying requirements in textual manner is common in software development. This leads to poor understanding of the requirements which results in more errors and degraded quality. There are some literatures which focus on semantic way of specifying functional requirement which ensure the consistency and completeness of requirements. Alternately in the work, a method is proposed to map the syntactic requirements with corresponding semantics in the form of ontologies. This improves the understanding of requirements, prevents errors and improves quality.

Keywords: functional requirement, ontology, requirements management, semantics

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Authors: Nicole Merkle, Stefan Zander

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Ambient Assisted Living (AAL) describes a technological and methodological stack of (e.g. formal model-theoretic semantics, rule-based reasoning and machine learning), different aspects regarding the behavior, activities and characteristics of humans. Hence, a semantic representation of the user environment and its relevant elements are required in order to allow assistive agents to recognize situations and deduce appropriate actions. Furthermore, the user and his/her characteristics (e.g. physical, cognitive, preferences) need to be represented with a high degree of expressiveness in order to allow software agents a precise evaluation of the users’ context models. The correct interpretation of these context models highly depends on temporal, spatial circumstances as well as individual user preferences. In most AAL approaches, model representations of real world situations represent the current state of a universe of discourse at a given point in time by neglecting transitions between a set of states. However, the AAL domain currently lacks sufficient approaches that contemplate on the dynamic adaptions of context-related representations. Semantic representations of relevant real-world excerpts (e.g. user activities) help cognitive, rule-based agents to reason and make decisions in order to help users in appropriate tasks and situations. Furthermore, rules and reasoning on semantic models are not sufficient for handling uncertainty and fuzzy situations. A certain situation can require different (re-)actions in order to achieve the best results with respect to the user and his/her needs. But what is the best result? To answer this question, we need to consider that every smart agent requires to achieve an objective, but this objective is mostly defined by domain experts who can also fail in their estimation of what is desired by the user and what not. Hence, a smart agent has to be able to learn from context history data and estimate or predict what is most likely in certain contexts. Furthermore, different agents with contrary objectives can cause collisions as their actions influence the user’s context and constituting conditions in unintended or uncontrolled ways. We present an approach for dynamically updating a semantic model with respect to the current user context that allows flexibility of the software agents and enhances their conformance in order to improve the user experience. The presented approach adapts rules by learning sensor evidence and user actions using probabilistic reasoning approaches, based on given expert knowledge. The semantic domain model consists basically of device-, service- and user profile representations. In this paper, we present how this semantic domain model can be used in order to compute the probability of matching rules and actions. We apply this probability estimation to compare the current domain model representation with the computed one in order to adapt the formal semantic representation. Our approach aims at minimizing the likelihood of unintended interferences in order to eliminate conflicts and unpredictable side-effects by updating pre-defined expert knowledge according to the most probable context representation. This enables agents to adapt to dynamic changes in the environment which enhances the provision of adequate assistance and affects positively the user satisfaction.

Keywords: ambient intelligence, machine learning, semantic web, software agents

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Authors: Hsien-Tsen Wang

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Business Intelligence (BI) has long helped organizations make informed decisions based on data-driven insights and gain competitive advantages in the marketplace. In the past two decades, businesses witnessed not only the dramatically increasing volume and heterogeneity of business data but also the emergence of new technologies, such as Artificial Intelligence (AI), Semantic Web (SW), Cloud Computing, and Big Data. It is plausible that the convergence of these technologies would bring more value out of business data by establishing linked data frameworks and connecting in ways that enable advanced analytics and improved data utilization. In this paper, we first review and summarize current BI applications and methodology. Emerging technologies that can be integrated into BI applications are then discussed. Finally, we conclude with a proposed synergy framework that aims at achieving a more flexible, scalable, and intelligent BI solution.

Keywords: business intelligence, artificial intelligence, semantic web, big data, cloud computing

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Authors: Yoon-Cheol Park

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This research focuses on examining the information structures in the old Chinese character-Korean translation of the Vajrachedika-prajna-paramita sutra. The background of this research comes from the fact that there were no previous researches which looked into the information structures in the target text of the Vajrachedika-prajna-paramita sutra by now. The existing researches on the Buddhist scripture translation mainly put weight on message conveyance by literal and semantic translation methods. But the message conveyance from one language to another has a necessity to be delivered with equivalent information structure. Thus, this research is intended to investigate on the flow of old and new information in the target text of Buddhist scripture, compared with source text. The Vajrachedika-prajna-paramita sutra unlike other Buddhist scriptures is composed of conversational structures between Buddha and his disciple, Suboli. This implies that the information flow can be changed by utterance context and some propositions. So, this research tries to analyze the flow of old and new information within the source and target text. As a result of analysis, this research can discover the following facts; firstly, there are the differences of the information flow in the message conveyance between the old Chinese character and Korean by language features. The old Chinese character reveals that old-new information flow is developed, while Korean indicates new-old information flow because of word order. Secondly, the source text of the Vajrachedika-prajna-paramita sutra includes abstruse terminologies, jargon and abstract words. These make influence on the target text and cause the change of the information flow. But the repetitive expressions of these words provide the old information in the target text. Lastly, the Vajrachedika-prajna-paramita sutra offers the expository structure from conversations between Buddha and Suboli. It means that the information flow is developed in the way of explaining specific subjects and of paraphrasing unfamiliar phrases and expressions. From the results of analysis above, this research can verify that the information structures in the target text of the Vajrachedika-prajna-paramita sutra are changed by specific subjects and terminologies, developed with the new-old information flow by repetitive expressions or word order and reveal the information structures familiar to target culture. It also implies that the translation of the Vajrachedika-prajna-paramita sutra as a religious book needs the message conveyance to take into account the information structures of two languages.

Keywords: abstruse terminologies, the information structure, new and old information, old Chinese character-Korean translation

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Authors: Pooja Khanal, Huaming Zhang

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Labeling of issues/bugs, also known as bug classification, plays a vital role in software engineering. Some known labels/classes of bugs are 'User Interface', 'Security', and 'API'. Most of the time, when a reporter reports a bug, they try to assign some predefined label to it. Those issues are reported for a project, and each project is a repository in GitHub/GitLab, which contains multiple issues. There are many software project repositories -ranging from individual projects to commercial projects. The labels assigned for different repositories may be dependent on various factors like human instinct, generalization of labels, label assignment policy followed by the reporter, etc. While the reporter of the issue may instinctively give that issue a label, another person reporting the same issue may label it differently. This way, it is not known mathematically if a label in one repository is similar or different to the label in another repository. Hence, the primary goal of this research is to find the semantic differences between bug labeling of different repositories via machine learning. Independent optimal classifiers for individual repositories are built first using the text features from the reported issues. The optimal classifiers may include a combination of multiple classifiers stacked together. Then, those classifiers are used to cross-test other repositories which leads the result to be deduced mathematically. The produce of this ongoing research includes a formalized open-source GitHub issues database that is used to deduce the similarity of the labels pertaining to the different repositories.

Keywords: bug classification, bug labels, GitHub issues, semantic differences

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Authors: Nutthapat Kaewrattanapat, Wannarat Bunchongkien

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The purposes of this research are to study and develop the algorithm of Thai spoonerism words by semi-automatic computer programs, that is to say, in part of data input, syllables are already separated and in part of spoonerism, the developed algorithm is utilized, which can establish rules and mechanisms in Thai spoonerism words for bi-syllables by utilizing analysis in elements of the syllables, namely cluster consonant, vowel, intonation mark and final consonant. From the study, it is found that bi-syllable Thai spoonerism has 1 case of spoonerism mechanism, namely transposition in value of vowel, intonation mark and consonant of both 2 syllables but keeping consonant value and cluster word (if any). From the study, the rules and mechanisms in Thai spoonerism word were applied to develop as Thai spoonerism word software, utilizing PHP program. the software was brought to conduct a performance test on software execution; it is found that the program performs bi-syllable Thai spoonerism correctly or 99% of all words used in the test and found faults on the program at 1% as the words obtained from spoonerism may not be spelling in conformity with Thai grammar and the answer in Thai spoonerism could be more than 1 answer.

Keywords: algorithm, spoonerism, computational linguistics, Thai spoonerism

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Authors: Moquan Gong

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In recent years, with the rapid development of deep learning, pre-training technology for natural language processing has made great progress. The early field of natural language processing has long used word vector methods such as Word2Vec to encode text. These word vector methods can also be regarded as static pre-training techniques. However, this context-free text representation brings very limited improvement to subsequent natural language processing tasks and cannot solve the problem of word polysemy. ELMo proposes a context-sensitive text representation method that can effectively handle polysemy problems. Since then, pre-training language models such as GPT and BERT have been proposed one after another. Among them, the BERT model has significantly improved its performance on many typical downstream tasks, greatly promoting the technological development in the field of natural language processing, and has since entered the field of natural language processing. The era of dynamic pre-training technology. Since then, a large number of pre-trained language models based on BERT and XLNet have continued to emerge, and pre-training technology has become an indispensable mainstream technology in the field of natural language processing. This article first gives an overview of pre-training technology and its development history, and introduces in detail the classic pre-training technology in the field of natural language processing, including early static pre-training technology and classic dynamic pre-training technology; and then briefly sorts out a series of enlightening technologies. Pre-training technology, including improved models based on BERT and XLNet; on this basis, analyze the problems faced by current pre-training technology research; finally, look forward to the future development trend of pre-training technology.

Keywords: natural language processing, pre-training, language model, word vectors

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Authors: Shyam Shukla

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A brand is the identity of a specific product, service or business. A brand can take many forms, including a name, sign, symbol, color, combination or slogan. The word brand began simply as a way to tell one person's identity from another by means of a hot iron stamp. A legally protected brand name is called a trademark. The word brand has continued to evolve to encompass identity - it affects the personality of a product, company or service. A concept brand is a brand that is associated with an abstract concept, like AIDS awareness or environmentalism, rather than a specific product, service, or business. A commodity brand is a brand associated with a commodity1. In this paper, it is tried to explore the significance of an intellectual capital for the branding of an Institution.

Keywords: brand, commodity, consumer, cultural values, intellectual capital, zonal cluster

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Authors: Baba Mbaye

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In collaborative filtering recommendation systems, a user receives suggested items based on the opinions and evaluations of a community of users. This type of recommendation system uses only the information (notes in numerical values) contained in a usage matrix as input data. This matrix can be constructed based on users' behaviors or by offering users to declare their opinions on the items they know. The cold start problem leads to very poor performance for new users. It is a phenomenon that occurs at the beginning of use, in the situation where the system lacks data to make recommendations. There are three types of cold start problems: cold start for a new item, a new system, and a new user. We are interested in this article at the cold start for a new user. When the system welcomes a new user, the profile exists but does not have enough data, and its communities with other users profiles are still unknown. This leads to recommendations not adapted to the profile of the new user. In this paper, we propose an approach that improves cold start by using the notions of similarity and semantic proximity between users profiles during cold start. We will use the cold-metadata available (metadata extracted from the new user's data) useful in positioning the new user within a community. The aim is to look for similarities and semantic proximities with the old and current user profiles of the system. Proximity is represented by close concepts considered to belong to the same group, while similarity groups together elements that appear similar. Similarity and proximity are two close but not similar concepts. This similarity leads us to the construction of similarity which is based on: a) the concepts (properties, terms, instances) independent of ontology structure and, b) the simultaneous representation of the two concepts (relations, presence of terms in a document, simultaneous presence of the authorities). We propose an ontology, OIVCSRS (Ontology of Improvement Visitor Cold Start in Recommender Systems), in order to structure the terms and concepts representing the meaning of an information field, whether by the metadata of a namespace, or the elements of a knowledge domain. This approach allows us to automatically attach the new user to a user community, partially compensate for the data that was not initially provided and ultimately to associate a better first profile with the cold start. Thus, the aim of this paper is to propose an approach to improving cold start using semantic technologies.

Keywords: visitor cold start, recommender systems, collaborative filtering, semantic filtering

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Authors: Sandhya Baskaran, Hari Kumar Nagabushanam

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Sarcasm sentiment recognition is an area of natural language processing that is being probed into in the recent times. Even with the advancements in NLP, typical translations of words, sentences in its context fail to provide the exact information on a sentiment or emotion of a user. For example, if something bad happens, the statement ‘That's just what I need, great! Terrific!’ is expressed in a sarcastic tone which could be misread as a positive sign by any text-based analyzer. In this paper, we are presenting a unique real time ‘word with its tone’ spotting technique which would provide the sentiment analysis for a tone or pitch of a voice in combination with the words being expressed. This hybrid approach increases the probability for identification of special sentiment like sarcasm much closer to the real world than by mining text or speech individually. The system uses a tone analyzer such as YIN-FFT which extracts pitch segment-wise that would be used in parallel with a speech recognition system. The clustered data is classified for sentiments and sarcasm score for each of it determined. Our Simulations demonstrates the improvement in f-measure of around 12% compared to existing detection techniques with increased precision and recall.

Keywords: sarcasm recognition, tone-word spotting, natural language processing, pitch analyzer

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Authors: Abrar Hussain Qureshi

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Vocabulary has been highlighted as a key indicator in any foreign language learning program, especially English as a foreign language (EFL). It is often considered a potential tool in foreign language curriculum, and its deficiency impedes successful communication in the target language. The knowledge of the lexicon is very significant in getting communicative competence and performance. Nouns constitute a considerable bulk of English vocabulary. Rather, they are the bones of the English language and are the main semantic carrier in spoken and written discourse. As nouns dominate the bulk of the English lexicon, their role becomes all the more potential. The undertaken research is a systematic effort in this regard to work out a list of highly frequent list of Pakistani English nouns for the EFL learners at the secondary level. It will encourage autonomy for the EFL learners as well as will save their time. The corpus used for the research has been developed locally from leading English newspapers of Pakistan. Wordsmith Tools has been used to process the research data and to retrieve word list of frequent Pakistani English nouns. The retrieved list of core Pakistani English nouns is supposed to be useful for English language learners at the secondary level as it covers a wide range of speech events.

Keywords: corpus, EFL, frequency list, nouns

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Authors: Carlo Gadingan

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Swardspeak, as a language, highlights the exclusive identity of the Filipino gay men and the oppression they are confronted in the society. This paper presents a morphological analysis of swardspeak in the Philippines. Specifically, it aims to find out the common morphological processes involved in the construction of codes that may unmask the nature of swardspeak as a language. 30 purposively selected expert users of swardspeak from Luzon, Visayas, and Mindanao were asked to codify 30 natural words through the Facebook Messenger application. The results of the structural analysis affirm that swardspeak follows no specific rules revealing complicated combinations of clipping/stylized clipping, borrowing, connotation through images, connotation through actions, connotation through sounds, affixation, repetition, substitution, and simple reversal. Moreover, it was also found out that most of these word formation processes occur in all word classes which indicate that swardspeak is very unpredictable. Although different codes are used for the same words, there are still codes that are really common to all homosexuals and these are Chaka (ugly), Crayola (cry), and Aida (referring to a person with AIDS). Hence, the prevailing word formation processes explored may be termed as observed time-specific patterns because the codes documented in this study may turn obsolete and may be replaced with novel ones in a matter of weeks to month, knowing the creativity of homosexuals and the multiplicity of societal resources which can be used to make the codes more opaque and more confusing for non-homosexuals.

Keywords: codes, homosexuals, morphological processes, swardspeak

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Authors: Shen Jian, Hu Jie, Ma Jin, Peng Ying Hong, Fang Yi, Liu Wen Hai

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Biologically inspired design inspires inventions and new technologies in the field of engineering by mimicking functions, principles, and structures in the biological domain. To deal with the obstacles of cross-domain knowledge acquisition in the existing biologically inspired design process, functional semantic clustering based on functional feature semantic correlation and environmental constraint clustering composition based on environmental characteristic constraining adaptability are proposed. A knowledge cell clustering algorithm and the corresponding prototype system is developed. Finally, the effectiveness of the method is verified by the visual prosthetic device design.

Keywords: knowledge clustering, knowledge acquisition, knowledge based engineering, knowledge cell, biologically inspired design

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Authors: Fetah Eren, Ismail Sahin, Ismail Celik, Ahmet Oguz Akturk

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The aim of this study is to discover secondary school students’ perceptions related to information technologies and the connections between concepts in their cognitive structures. A word association test consisting of six concepts related to information technologies is used to collect data from 244 secondary school students. Concept maps that present students’ cognitive structures are drawn with the help of frequency data. Data are analyzed and interpreted according to the connections obtained as a result of the concept maps. It is determined students associate most with these concepts—computer, Internet, and communication of the given concepts, and associate least with these concepts—computer-assisted education and information technologies. These results show the concepts, Internet, communication, and computer, are an important part of students’ cognitive structures. In addition, students mostly answer computer, phone, game, Internet and Facebook as the key concepts. These answers show students regard information technologies as a means for entertainment and free time activity, not as a means for education.

Keywords: word association test, cognitive structure, information technology, secondary school

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Authors: C. B. Abhilash, Kavi Mahesh

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Human beings are the most advanced species on earth; it's all because of the ability to communicate and share information via human language. In today's world, a huge amount of data is available on the web in text format. This has also resulted in the generation of big data in structured and unstructured formats. In general, the data is in the textual form, which is highly unstructured. To get insights and actionable content from this data, we need to incorporate the concepts of text mining and natural language processing. In our study, we mainly focus on Interesting data through which interesting facts are generated for the knowledge base. The approach is to derive the analytics from the text via the application of natural language processing. Using semantic web Resource framework descriptors (RDF), we generate the triple from the given data and derive the interesting patterns. The methodology also illustrates data integration using the RDF for reliable, interesting patterns.

Keywords: RDF, interestingness, knowledge base, semantic data

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Authors: Milind Patil

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Present paper is a discussion on Hindi-Marathi Morphological Analysis and generating rules for Machine Translation on the basis of Machine Readable Dictionary (MRD). This used Transformative Generative Grammar (TGG) rules to design the MRD. As per TGG rules, the suffix of a particular root word is based on its Tense, Aspect, Modality and Voice. That's why the suffix is very important for the word meanings (or root meanings). The Hindi and Marathi Language both have relation with Indo-Aryan language family. Both have been derived from Sanskrit language and their script is 'Devnagari'. But there are lots of differences in terms of semantics and grammatical level too. In Marathi, there are three genders, but in Hindi only two (Masculine and Feminine), the Natural gender is absent in Hindi. Likewise other grammatical categories also differ in their level of use. For MRD the suffixes (or Morpheme) are of particular root word for GNP (Gender, Number and Person) are based on its natural phenomena. A particular Suffix and Morphine change as per the need of person, number and gender. The design of MRD also based on this format. In first, Person, Number, Gender and Tense are key points than root words and suffix of particular Person, Number Gender (PNG). After that the inferences are drawn on the basis of rules that is (V.stem) (Pre.T/Past.T) (x) + (Aux-Pre.T) (x) → (V.Stem.) + (SP.TM) (X).

Keywords: MRD, TGG, stem, morph, morpheme, suffix, PNG, TAM&V, root

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Authors: Changyin Zhou

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Ba constructions are a special type of constructions in Chinese. Their syntactic behaviors are closely related to their event structural properties. The existing study which treats the semantic function of Ba as causative meets difficulty in treating the discrepancy between Ba constructions and their corresponding constructions without Ba in expressing causativity. This paper holds that Ba in Ba constructions is a functional category expressing affectedness. The affectedness expressed by Ba can be positive or negative. The functional category Ba expressing negative affectedness has the semantic property of being 'expected'. The precondition of Ba construction is the boundedness of the event concerned. This paper, holding the parallelism between motion events and change-of-state events, proposes a syntactic model based on the notions of boundedness and affectedness, discusses the transformations between Ba constructions and the related resultative constructions, and derivates the various Ba constructions concerned.

Keywords: affectedness, Ba constructions, boundedness, event structure, resultative constructions

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Authors: Marina Yurievna Aleksandrova

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Research of the Holocaust retains value not only for history but also for sociology and psychology. One of the most important fields of study is how people were coping during and after this traumatic event. The aim of this paper is to identify the main contexts of the topic of love and to determine which contexts are more characteristic for different groups of victims of the Holocaust (gender, nationality, age). In this research, transcripts of interviews with Holocaust victims that were collected during 1946 for the "Voices of the Holocaust" project were used as data. Main contexts were analyzed with methods of network analysis and latent semantic analysis and classified by gender, age, and nationality with random forest. The results show that love is articulated and described significantly differently for male and female informants, nationality is shown results with lower values of quality metrics, as well as the age.

Keywords: Holocaust, latent semantic analysis, network analysis, text-mining, random forest

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Authors: Fang Tan

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This research focuses on the appropriate use of lexical bundles in spoken discourse, particularly in English as a Foreign Language (EFL) classrooms in Northeast China. While previous studies have mainly examined lexical bundles in written discourse, there is a need to investigate their usage in spoken discourse due to the limited availability of spoken discourse corpora. English teachers’ use of lexical bundles is crucial for effective teaching and communication in the EFL classroom. The aim of this study is to investigate the functions of four-word lexical bundles in native English teachers’ EFL oral English classes in Northeast China. Specifically, the research focuses on the usage of stance bundles, which were found to be the most significant type of bundle in the analyzed corpus. By comparing the self-built university spoken English classroom discourse corpus with the other self-built university English for General Purposes (EGP) corpus, the study aims to highlight the difference in bundle usage between native and non-native teachers in EFL classrooms. The research employs a corpus-based study. The observed corpus consists of more than 300,000 tokens, in which the data has been collected in the past five years. The reference corpus is composed of over 800,000 tokens, in which the data has been collected over 12 years. All the primary data collection involved transcribing and annotating spoken English classes taught by native English teachers. The analysis procedures included identifying and categorizing four-word lexical bundles, with specific emphasis on stance bundles. Frequency counts, and comparisons with the Chinese English teachers’ corpus were conducted to identify patterns and differences in bundle usage. The research addresses the following questions: 1) What are the functions of four-word lexical bundles in native English teachers’ EFL oral English classes? 2) How do stance bundles differ in usage between native and non-native English teachers’ classes? 3) What implications can be drawn for English teachers’ professional development based on the findings? In conclusion, this study provides valuable insights into the usage of four-word lexical bundles, particularly stance bundles, in native English teachers’ EFL oral English classes in Northeast China. The research highlights the difference in bundle usage between native and non-native English teachers’ classes and provides implications for English teachers’ professional development. The findings contribute to the understanding of lexical bundle usage in EFL classroom discourse and have theoretical importance for language teaching methodologies. The self-built university English classroom discourse corpus used in this research is a valuable resource for future studies in this field.

Keywords: EFL classroom discourse, four-word lexical bundles, stance, implication

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Authors: Dania Aljouhi

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September 26th, 2017, is a historic date for all women in Saudi Arabia. On that day, Saudi Arabia announced the decree on allowing Saudi women to drive. With the advent of vision 2030 and its goal to empower women and increase their participation in Saudi society, we see how Saudis’ Twitter users deliberate the 2017 decree from different social, cultural, religious, economic and political factors. This topic bridges social media 'Twitter,' gender and social-cultural studies to offer insights into how Saudis’ tweets reflect a broader discourse on Saudi women in the age of social media. The present study aims to explore the meanings and themes that emerge by Saudis’ Twitter users in response to the 2017 royal decree on women driving. The sample used in the current study involves (n= 1000) tweets that were collected from Sep 2017 to March 2019 to account for the Saudis’ tweets before and after implementing the decree. The paper uses semantic and thematic network analysis methods to examine the Saudis’ Twitter discourse on the women driving issue. The paper argues that Twitter as a platform has mediated the discourse of women driving among the Saudi community and facilitated social changes. Finally, framing theory (Goffman, 1974) and Networked framing (Meraz & Papacharissi 2013) are both used to explain the tweets on the decree of allowing Saudi women to drive based on # Saudi women-driving-cars.

Keywords: Saudi Arabia, women, Twitter, semantic network analysis, framing

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Authors: Cemil Kutluturk

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This is a comparative study of the incarnation of God according to Hinduism and Christianity. After dealing with their basic ideas on the concept of the incarnation of God, the main similarities and differences between each other will be examined by quoting references from their sacred texts. In Hinduism, the term avatara is used in order to indicate the concept of the incarnation of God. The word avatara is derived from ava (down) and tri (to cross, to save, attain). Thus avatara means to come down or to descend. Although an avatara is commonly considered as an appearance of any deity on earth, the term refers particularly to descents of Vishnu. According to Hinduism, God becomes an avatara in every age and entering into diverse wombs for the sake of establishing righteousness. On the Christian side, the word incarnation means enfleshment. In Christianity, it is believed that the Logos or Word, the Second Person of Trinity, presumed human reality. Incarnation refers both to the act of God becoming a human being and to the result of his action, namely the permanent union of the divine and human natures in the one Person of the Word. When the doctrines of incarnation and avatara are compared some similarities and differences can be found between each other. The basic similarity is that both doctrines are not bound by the laws of nature as human beings are. They reveal God’s personal love and concern, and emphasize loving devotion. Their entry into the world is generally accompanied by extraordinary signs. In both cases, the descent of God allows for human beings to ascend to God. On the other hand, there are some distinctions between two religious traditions. For instance, according to Hinduism there are many and repeated avataras, while Christ comes only once. Indeed, this is related to the respective cyclic and linear worldviews of the two religions. Another difference is that in Hinduism avataras are real and perfect, while in Christianity Christ is also real, yet imperfect; that is, he has human imperfections, except sin. While Christ has never been thought of as a partial incarnation, in Hinduism there are some partial and full avataras. The other difference is that while the purpose of Christ is primarily ultimate salvation, not every avatara grants ultimate liberation, some of them come only to save a devotee from a specific predicament.

Keywords: Avatara, Christianity, Hinduism, incarnation

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Authors: Kishore Kumar R., Kaustav Sengupta, Shalini Sood Sehgal, Poornima Santhanam

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Fashion is a human expression that is constantly changing. One of the prime factors that consistently influences fashion is the change in colour preferences. The role of colour in our everyday lives is very significant. It subconsciously explains a lot about one’s mindset and mood. Analyzing the colours by extracting them from the outfit images is a critical study to examine the individual’s/consumer behaviour. Several research works have been carried out on extracting colours from images, but to the best of our knowledge, there were no studies that extract colours to specific apparel and identify colour patterns geographically. This paper proposes a framework for accurately extracting colours from T-shirt images and predicting dominant colours geographically. The proposed method consists of two stages: first, a U-Net deep learning model is adopted to segment the T-shirts from the images. Second, the colours are extracted only from the T-shirt segments. The proposed method employs the iMaterialist (Fashion) 2019 dataset for the semantic segmentation task. The proposed framework also includes a mechanism for gathering data and analyzing India’s general colour preferences. From this research, it was observed that black and grey are the dominant colour in different regions of India. The proposed method can be adapted to study fashion’s evolving colour preferences.

Keywords: colour analysis in t-shirts, convolutional neural network, encoder-decoder, k-means clustering, semantic segmentation, U-Net model

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Authors: Estelle Ardanouy, Helene Delage, Pascal Zesiger

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Morphological awareness contributes to the acquisition of reading and spelling in typical learners as well as in children with learning disorders. Indeed, the acquisition of phoneme-grapheme correspondences is not sufficient to master spelling, especially in inconsistent orthographic systems such as English or French. Several meta-analyses show the benefit of explicit training in derivational morphology on reading and spelling in old children (who have already learned the main grapheme-phoneme correspondences), but highlight the lack of studies with younger children, particularly in French. In this study, we chose to focus on the efficiency of an intensive training in derivational morphology on spelling skills in French-speaking four-graders (9-10 years of age). The training consisted of 1) learning how to divide words into morphemes (ex: para/pente in French, paraglider in English), as well as 2) working on the meaning of affixes in relation to existing words (ex: para/pente: to protect against – para - the slope -pente). One group of pupils (N = 37, M age = 9.5) received this experimental group training in morphology while an alternative training group (N = 34, M age = 9.6) received a visuo-semantic training based on visual cues to memorize the spelling difficulties of complex words (such as the doubling of “r” in “verre” in French -or "glass" in English-which are represented by the drawing of two glasses). Both trainings lasted a total of 15 hours at a rate of four 45 minutes sessions per week, resulting in five weeks of training in the school setting. Our preliminary results show a significant improvement in the experimental group in the spelling of affixes on the trained (p < 0.001) and untrained word lists (p <0.001), but also in the root of words on the trained (p <0.001) and untrained word lists group (p <0.001). The training effect is also present on both trained and untrained morphologically composed words. By contrast, the alternative training group shows no progress on these previous measures (p >0.15). Further analyses testing the effects of both trainings on other measures such as morphological awareness and reading of morphologically compose words are in progress. These first results support the effectiveness of explicitly teaching derivational morphology to improve spelling in school-aged children. The study is currently extended to a group of children with developmental dyslexia because these children are known for their severe and persistent spelling difficulties.

Keywords: developmental dyslexia, derivational morphology, reading, school-aged children, spelling, training

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Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

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Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

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Authors: Mina Samizadeh

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In the past few decades, there has been an explosion in the amount of available data produced from various sources with different topics. The availability of this enormous data necessitates us to adopt effective computational tools to explore the data. This leads to an intense growing interest in the research community to develop computational methods focused on processing this text data. A line of study focused on condensing the text so that we are able to get a higher level of understanding in a shorter time. The two important tasks to do this are keyword extraction and text summarization. In keyword extraction, we are interested in finding the key important words from a text. This makes us familiar with the general topic of a text. In text summarization, we are interested in producing a short-length text which includes important information about the document. The TextRank algorithm, an unsupervised learning method that is an extension of the PageRank (algorithm which is the base algorithm of Google search engine for searching pages and ranking them), has shown its efficacy in large-scale text mining, especially for text summarization and keyword extraction. This algorithm can automatically extract the important parts of a text (keywords or sentences) and declare them as a result. However, this algorithm neglects the semantic similarity between the different parts. In this work, we improved the results of the TextRank algorithm by incorporating the semantic similarity between parts of the text. Aside from keyword extraction and text summarization, we develop a topic clustering algorithm based on our framework, which can be used individually or as a part of generating the summary to overcome coverage problems.

Keywords: keyword extraction, n-gram extraction, text summarization, topic clustering, semantic analysis

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