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Meme marketing taps into the potential of digital culture. Despite considerable practitioner interest, meme marketing is under-researched. The present study aims to fulfil two purposes. First, to conceptualise meme marketing, we draw on speech act theory and the theory of consumption values to define meme marketing. On this basis, we differentiate meme marketing from relevant digital marketing constructs, including e-WOM, digital content marketing, and viral marketing. Second, to validate the proposed definition, we empirically perform a qualitative content analysis to taxonomize meme marketing speech acts. Based on the content analysis of 699 meme marketing posts, we found that meme marketing serves as a rich communication source, sending intentions of the brand through single (i.e. assertive, directive, expressive) as well as combined (i.e. assertive entailing expressive, assertive entailing directive) speech acts that provide customers with epistemic, emotional, and social values. Important theoretical and managerial implications for academic researchers and marketers are also discussed. [ABSTRACT FROM AUTHOR]

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The technological pedagogical content knowledge (TPACK) framework is a prominent framework for describing the knowledge teachers require for teaching in the digital era. Recently, the component of “context” was officially recognized as an additional domain of knowledge. Since then, this domain has started to gradually gain more attention in TPACK research. Yet research on contextual knowledge (XK) appears somewhat sporadic, indicating the need for greater clarity on this construct. As the domain representing the knowledge of educational contexts, the role of experience in educational contexts emerges as a key point for shedding light on this construct. The present systematic literature review aimed to offer an overview of the representations and functions associated with XK through the lens of experience and investigated this domain through comparing the empirical literature focusing on preservice teachers (less experienced) to that focusing on inservice teachers (more experienced). Systematically screening the literature resulted in a final sample of 15 studies conducted among preservice teachers and 23 on inservice teachers. Records were analyzed through qualitative codings and quantitative comparisons of these codes between these two groups of studies. Findings revealed a consistent multifaceted structure of XK across both inservice and preservice teacher studies with a distinctive greater focus on school-level factors in inservice teacher studies. In addition, across groups, studies provided evidence confirming XK as a domain supporting teachers’ practice, that can be developed, and that relates to other pedagogically relevant constructs. These findings are discussed with regards to their implications for researchers, practitioners, and stakeholders.

The paper reports a study, which explored teachers' perceptions and experiences on integration of cultural belief systems when teaching life sciences. Beliefs have been found to influence teachers' pedagogical practices and ultimately impact on learning and understanding of concepts. These beliefs can be classified as originating from one's religion or one's culture. Life sciences is a subject that deals with real life issues hence has topics that challenge or impact on one's belief systems. In a qualitative case study research design, 20 experienced life sciences (biology) teachers were purposively selected for the study. Each teacher was interviewed, and data was subjected to content analysis. Findings showed that though teachers strive to suppress their own belief systems in the classrooms, both teachers' and learners' beliefs need to be integrated when teaching certain topics. The practice enhances development of critical thinking skills and ultimately learner understanding. These findings allude to the need for teachers' development in employing contextually relevant pedagogical strategies. [ABSTRACT FROM AUTHOR]

Over the last fifty years, societies across the world have experienced multiple periods of energy insufficiency with the most recent one being the 2022 global energy crisis. In addition, the electric power industry has been experiencing a steady increase in electricity consumption since the second industrial revolution because of the widespread usage of electrical appliances and devices. Newer devices are equipped with sensors and actuators, they can collect a large amount of data that could help in power management. However, current energy management approaches are mostly applied to limited types of devices in specific domains and are difficult to implement in other scenarios. They fail when it comes to their level of autonomy, flexibility, and genericity. To address these shortcomings, we present, in this paper, an automated energy management approach for connected environments based on generating power estimation models, representing a formal description of energy-related knowledge, and using reinforcement learning (RL) techniques to accomplish energy-efficient actions. The architecture of this approach is based on three main components: power estimation models, knowledge base, and intelligence module. Furthermore, we develop algorithms that exploit knowledge from both the power estimator and the ontology, to generate the corresponding RL agent and environment. We also present different reward functions based on user preferences and power consumption. We illustrate our proposal in the smart home domain. An implementation of the approach is developed and two validation experiments are conducted. Both case studies are deployed in the context of smart homes: (a) a living room with a variety of devices and (b) a smart home with a heating system. The obtained results show that our approach performs well given the low convergence period, the high level of user preferences satisfaction, and the significant decrease in energy consumption. [ABSTRACT FROM AUTHOR]

In recent years, Mongolian-Chinese neural machine translation (MCNMT) technology has made substantial progress. However, the establishment of the Mongolian dataset requires a significant amount of financial and material investment, which has become a major obstacle to the performance of MCNMT. Pre-training and fine-tuning technology have also achieved great success in the field of natural language processing, but how to fully exploit the potential of pre-training language models (PLMs) in MCNMT has become an urgent problem to be solved. Therefore, this paper proposes a novel MCNMT model based on the soft target template and contextual knowledge. Firstly, to learn the grammatical structure of target sentences, a selection-based parsing tree is adopted to generate candidate templates that are used as soft target templates. The template information is merged with the encoder-decoder framework, fully utilizing the templates and source text information to guide the translation process. Secondly, the translation model learns the contextual knowledge of sentences from the BERT pre-training model through the dynamic fusion mechanism and knowledge extraction paradigm, so as to improve the model's utilization rate of language knowledge. Finally, the translation performance of the proposed model is further improved by integrating contextual knowledge and soft target templates by using a scaling factor. The effectiveness of the modified model is verified by a large number of data experiments, and the calculated BLEU (BiLingual Evaluation Understudy) value is increased by 4.032 points compared with the baseline MCNMT model of Transformers. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze Nepali English teachers’ narrations and reflections about good English language teaching which they most enjoyed and affected their career choices. The present paper adopted narrative inquiry as a research design. The study was conducted with five participants who unfolded stories and memoirs on how they learned English. The participants shared their experiences which left indelible marks on their minds. The data were analyzed thematically in such a way that their conceptions of quality English teaching were reflected. The study indicated that teachers are instrumental in enabling learning, and they succeed most when they exhibit the behaviors that lead to the desired learning outcomes, such as excellent grades, favorable attitudes toward learning, and improved learning abilities. Good teaching is not a static, stereotyped activity that can only be accomplished by using instructional techniques. Quality English language teaching cannot be imagined without quality English teachers who are required to possess competencies like strong subject-matter expertise, pedagogical skills, contextual knowledge, passion, and the capacity to foster a friendly learning atmosphere so that they can maximize learning and are better able to accomplish increased learning. Thus, quality English language teaching places learners at the center of the learning process. [ABSTRACT FROM AUTHOR]

This paper investigates how pragmatics can be deployed to interpret non-standard usages in the context of the English language users. There are two important linguistics keys to interpreting meanings in English: pragmatics and grammatical competence. For effective communication, a decoder must be competent at the two levels. Many researchers in Nigeria have agreed that, Nigerian English has come to stay, therefore, we need to look beyond the grammatical rules and deploy a method of interpreting non-standard usages in the messages of these people. Decoding a message does not rely only on the linguistics or signs, there are extra linguistic properties that help the addressee to infer meanings from the message of the speakers which can be drawn from pragmatics. Pragmatics and grammar work hand in hand to provide meanings during an interaction.

In recent years, Mongolian-Chinese neural machine translation (MCNMT) technology has made substantial progress. However, the establishment of the Mongolian dataset requires a significant amount of financial and material investment, which has become a major obstacle to the performance of MCNMT. Pre-training and fine-tuning technology have also achieved great success in the field of natural language processing, but how to fully exploit the potential of pre-training language models (PLMs) in MCNMT has become an urgent problem to be solved. Therefore, this paper proposes a novel MCNMT model based on the soft target template and contextual knowledge. Firstly, to learn the grammatical structure of target sentences, a selection-based parsing tree is adopted to generate candidate templates that are used as soft target templates. The template information is merged with the encoder-decoder framework, fully utilizing the templates and source text information to guide the translation process. Secondly, the translation model learns the contextual knowledge of sentences from the BERT pre-training model through the dynamic fusion mechanism and knowledge extraction paradigm, so as to improve the model’s utilization rate of language knowledge. Finally, the translation performance of the proposed model is further improved by integrating contextual knowledge and soft target templates by using a scaling factor. The effectiveness of the modified model is verified by a large number of data experiments, and the calculated BLEU (BiLingual Evaluation Understudy) value is increased by 4.032 points compared with the baseline MCNMT model of Transformers.

Network human operators’ decision-making during grid outages requires significant attention and the ability to perceive real-time feedback from multiple information sources to minimize the number of control actions required to restore service, while maintaining the system and people safety. Data-driven event and alarm management have the potential to reduce human operator cognitive burden. However, the high complexity of events, the data semantics, and the large variety of equipment and technologies are key barriers for the application of Artificial Intelligence (AI) to raw SCADA data. In this context, this paper proposes a methodology to convert a large volume of alarm events into data mining terminology, creating the conditions for the application of modern AI techniques to alarm data. Moreover, this work also proposes two novel data-driven applications based on SCADA data: (i) identification of anomalous behaviors regarding the performance of the protection relays of primary substations, during circuit breaker tripping alarms in High Voltage (HV) and Medium Voltage (MV) lines; (ii) unsupervised learning to cluster similar events in HV line panels, classify new event logs based on the obtained clusters and membership grade with a control parameter that helps to identify rare events. Important aspects associated with data handling and pre-processing are also covered. The results for real data from a Distribution System Operator (DSO) showed: (i) that the proposed method can detect unexpected relay pickup events, e.g., one substation with nearly 41% of the circuit breaker alarms had an ‘atypical’ event in their context (revealed an overlooked problem on the electrification of a protection relay); (ii) capability to automatically detect and group issues into specific clusters, e.g., SF6 low-pressure alarms and blocks with abnormal profiles caused by event time-delay problems.

Students have a myriad of preferences of the various curricular elements especially teaching-learning methods, teaching aids and content. Taking radiological anatomy as a prototype, the responses from medical students in the clinical years regarding the rationale for preferences for the curricular elements were studied in a private university in Malaysia. The students' responses to the research question were analysed qualitatively through thematic analysis. Analysis was conducted until no new themes emerged and data saturation was complete. The final themes were subjected to investigator triangulation. While searching answers for our research question of why students prefer certain curricular elements, the sub-themes that emerged were deep approach to learning, enabling transferability of knowledge to workplace, managing cognitive load and suiting learning styles. These sub-themes were categorised into two large themes namely student interaction with learning environment and relevancy of content to context. This prototype study sheds light on students preferred curricular elements. Curricular elements which employ effective student interaction with the learning environment and ensure relevancy to the context are highly sought. It could be concluded that active learning entailing studentcentred methods and clinically relevant content is most preferred by students. This prototype study provides potential findings that benefit other disciplines as well. [ABSTRACT FROM AUTHOR]

Despite its 35 years of academic presence, practice-led research is still in the process of building its status regarding definitions and its discourse. One of the fundamentals of this type of research is that practice-led expands the study to encompass questions that emerge from practice. Practice-led maintain the scientific, academic protocols and observing academic rigour, following structures and themes with the characteristics of rigour: the study goals, the methods for specific media and processes, the substantiation of the process of discovery, and nevertheless, its historical and theoretical context. This article presents the contextual and historical review of knowledge of the practice-led project developed for my PhD project: ‘The process of immersive photography: beyond the cognitive and the physical’ and considers a process methodologically and conceptually, demonstrating its nature and the manner in which it occurs in a photographer’s practice. This process enables a deeply internal yet communicative interaction between the photographer and the land. The aim is to contextualise the main notions and concepts that informed the inquiry, and this article focuses on two of these concepts: immersion, and embodiment while contributes to clarity about the contextual and historical background that informed the research.

Machine learning is compelling in solving various applied problems. Nevertheless, machine learning methods lack the contextual reasoning capabilities and cannot be fitted to utilize additional information about circumstances, environments, backgrounds, etc. Such information provides essential knowledge about possible reasons for particular actions. This knowledge could not be processed directly by either machine learning methods. This paper presents the context-aware machine learning approach for actor behavior contextual reasoning analysis and context-based prediction for threat assessment. Moreover, the proposed approach uses context-aware prediction to tackle the interaction between actors. An idea of the technique lies in the cooperative use of two classification methods when one way predicts an actor's behavior. The second method discloses such predicted action (behavior) that is non-typical or unusual. Such integration of two-method allows the actor to make the self-awareness threat assessment based on relations between different actors where some multidimensional numerical data define the connections. This approach predicts the possible further situation and makes its threat assessment without any waiting for future actions. The suggested approach is based on the Decision Tree and Support Vector Method algorithm. Due to the complexity of context, marine traffic data was chosen to demonstrate the proposed approach capability. This technique could deal with the end-to-end approach for safe vessel navigation in maritime traffic with considerable ship congestion. [ABSTRACT FROM AUTHOR]

It is commonly known that social workers are required to possess wide-ranging knowledge. Furthermore, a multi-dimensional approach to social work knowledge is required to help social workers make decisions in their practice. Thus, for many social workers, it is almost impossible to emphasise only one dimension of knowledge over others. One area of social work that is of particular interest is resettlement for former refugees. The knowledge required for social workers practising in refugee resettlement must include contextual knowledge of both local and global contexts. This article is a reflection from a resettlement social worker, who shares her own practice journey in working with former refugees in Aotearoa New Zealand. As part of this reflection, the social worker shares her experience in utilising different types of knowledge to inform decision-making processes in her practice. She hopes this article can encourage further reflections in our social work practice with former refugees to become more culturally sensitive, effective and contextual to each social work scenario. [ABSTRACT FROM AUTHOR]

The purpose of the article is the creation of a typology of contexts based on the comparative analysis of the concept of contexts and classification of context types in various areas of theoretical and applied knowledge that reflect the specificity of the contextual approach trends in interdisciplinary research. The specificity of the subject/interdisciplinary areas of contexts extraction should be taken into account to interpret the context knowledge. For this objective, contexts systematization of text and non-textual modality has been done. The represented typology of context knowledge, automatically extracted from distributed information environment, (depending on research objectives) may be supplemented by the types of contexts, not being extracted automatically, but which perform important functions in the development of the subject area. In substantiating the typology of contexts and contextual knowledge, the synthetic method and method of content-analysis, the principles of systemic approach and ontological approach are used. The principles of classification and functions of contexts in Computer Science, various subject areas of scientific knowledge, and in the field of communication are represented. A typology of contexts, which includes different levels of contextual knowledge interpretation on the base of multi-level structural analysis, is formed. The devised typology of contexts including different levels of contextual knowledge interpretation is tested in the pilot study analysis of the terminological base development of interdisciplinary direction ‘Digital economy: the State e-Governance and Smart Technologies’. Contextual knowledge clusters of this subject area identified with the term-concepts, allow you distinguishing thematic trends to form the conceptual model of this interdisciplinary direction, and determine the tendencies of its development. Further work involves creating a thesaurus and subject domain ontology of ‘Digital Economy’. The results of the research can be used to analyze the development of terminological databases of promising interdisciplinary research directions that has important theoretical significance for Cognitive Linguistics, Semantics, Humanitarian Informatics. The principles of the contextual approach can be used in teaching practice to achieve self-actualization by students and in the field of social communications.

The competency-based approach conceives mathematics as a necessary tool for dealing with daily-life tasks. Many studies have focused on examining the low math competency people show when solving problems in real-life contexts, but rarely characterize the type of mathematics needed in these contexts and how people use this mathematics. The current study was designed to analyze the mathematics utilized by 312 customers when purchasing carpentry products in a store specialized in home projects. Aspects of the Anthropological Theory of the Didactic, especially the extended praxeological model, were employed to undertake the analysis. While the approach is primarily qualitative, quantitative aspects were also considered to elucidate the nature of the identified mathematics tasks, the techniques that customers employed to solve them, and the difficulties associated with the use of these techniques. The study reveals that having solid mathematical knowledge is insufficient when it comes to solving everyday tasks, because related contextual knowledge is also required. The nature of the tasks identified in this study and the didactic way in which the clerk guided the customers through the projects suggested that there are no only complex relationships between school mathematics and outside-school mathematics, but there exist also different didactics specific to the contexts. For elaborating the home carpentry projects, the customers needed to handle a set of carpentry knowledge and techniques as well as carpentry-related mathematics that are not necessarily taught at school. [ABSTRACT FROM AUTHOR]

In this article, we explore the multiple challenges faced by health service providers and immigrant patients when addressing sensitive issues in the context of HIV/STD counselling. To do so, we adopt a case-study approach which enables a deeply contextualised examination of the interactional process. The analysis will draw particular attention to the issue of contextual common ground, or the lack thereof, and how this correlates with the complex interactional conditions of the encounter. These conditions entail a combination of challenges, including the use of ad hoc interpreters on sensitive topics, in a remote, non-face-to-face constellation and with the use of digital support tools that are not equally accessible for all participants. We argue that this accumulation of multilingual, multimodal and multi-contextual challenges engenders the emergence of parallel tracks and limited consonance between institutional-counselling concerns on the one hand and experiential-patient concerns on the other. [ABSTRACT FROM AUTHOR]

Contribution: From the moment when universities began to talk about learning environment instead of classes and schools, a summative notion that entails knowledge transfer heralds an important stage of redefining the basic elements of learning. A learning environment requires the transition from learning rounding off toward open learning, without the limitations of time and space. Background: In this context, the present work attempts to examine the role of an instrument, namely, remote experiment (RE), and its application to draw attention to the pedagogical complexity embedded by this new experiential learning tool. Intended Outcomes: The objective is to prove that the transition from classical education with a defined location and geographical separation, to a modern e-learning education occurs by using the RE that contains all forms of knowledge. In addition, the operation tacitly and explicitly knowledge augments the importance of RE in applicative education as well as the didactic results in line with the industry requirements. Application Design: The convergent analysis method was used and emphasized the divergent content of knowledge acquired through the use of RE that is replete with a great creative potential. Findings: It is shown that its usage (a mix between software, hardware, and pedagogy) manages to address the students’ concerns through all ranges of knowledge that falls under a well-organized teaching system, can call. This underscores the importance of embedded knowledge that can expand students’ knowledge area in a happy manner. [ABSTRACT FROM AUTHOR]

On the face of it, it appears that the explicitation of contextual knowledge is consistent with Toury's (1995) norm of acceptability rather than his norm of adequacy. This is because this type of explicitation, which seeks to bridge the gap in readers' contextual knowledge, enhances readability and is directed towards the target audience. However, in this article I argue that the use of this type of explicitation actually demonstrates an awareness of the importance of adhering to the source text, and may thus be aligned with adequacy norms. To support the argument, I show that this type of explicitation seems to be more prevalent than ever before, using as an example a recent translation of a story by O. Henry into Hebrew, in the context of Hebrew translated literature generally moving towards the norm of adequacy (Zoran 1990; Weissbrod 1992; Ben-Shahar 1994). This highlights the complexity of the relation between increased explicitness and the notions of adequacy and acceptability. [ABSTRACT FROM AUTHOR]

Social and cultural context is largely missing in current language translation systems. Dictionary based systems translate terms in a source language to an equivalent term in a target language, but often the translation could be inaccurate when context is not taken into consideration, or when an equivalent term in the target language does not exist. Domain knowledge and context can be made explicit by using ontologies, and ontology utilization would enable inclusion of semantic relations to other terms, leading to translation results which is more comprehensive than a single equivalent term. It is proposed that existing ontologies in the domain should be utilized and combined by ontology merging techniques, to leverage on existing resources to form a basis ontology with contextual representation, and this can be further enhanced by using machine translation techniques on existing corpora to improve the basic ontology to append further contextual information to the knowledge base. Statistical methods in machine translation could provide automated relevance determination of these existing resources which are machine readable, and aid the human translator in establishing a domain specific knowledge base for translation. Advancements in communication and technologies has made the world smaller where people of different regions and languages need to work together and interact. Machine translation provides an automatic approach to make translation facilities available to anyone, at any time. The accuracy of these translations are crucial as it could lead to misunderstandings and possible conflict. While single equivalent terms in a target language can provide a gist of the meaning of a source language term, a semantic conceptualisation provided by an ontology could enable the term to be understood in the specific context that it is being used.

Today's industry force demands for knowledgeable and skillful engineering graduates in using modern Computer Aided Design (CAD) tools. However, current practices show that there is a lack of contextual knowledge among Mechanical Engineering undergraduates and fresh graduate engineers in utilizing 3D CAD modeling software to develop a good product design. Hence, a study to address this issue is conducted by focusing on the representation of contextual knowledge in 3D CAD modeling. This article focused on presenting essential elements of contextual knowledge utilized among practicing engineers in their daily design works, in the aspect of Model Visualization. This study was conducted using a transcendental phenomenology approach. Four practicing engineers from engineering department of one shipbuilding company in Peninsular Malaysia were purposively selected to be studied. From the analysis, there are three most frequent emerging themes in the application of contextual knowledge in visualizing and presenting model using 3D CAD modeling: Realization, Design Intention and Normalization. These three elements play an important role in helping engineers to contextualize their design work during the stage of visualizing and presenting created model for new product development process. [ABSTRACT FROM AUTHOR]

This monograph is on "Word Sense Disambiguation in Tamil". It is organized into six chapters. 1. Introduction, 2. Structural ambiguity and lexical ambiguity, 3. Machine translation and word sense disambiguation, 4. Word sense disambiguation processing and development, 5. Approaches to word sense disambiguation in Tamil, and 6. Conclusion. Chapter 1: Introduction The introduction deals with aims and objectives and the importance of word sense disambiguation in machine translation. Chapter 2: Structural ambiguity and lexical ambiguity There are two kinds of ambiguity: structural ambiguity and lexical ambiguity. If a sentence or phrase has more than one interpretation of meaning, it is called structural ambiguity. For example, the following two phrases can be interpreted to have at least two meanings. veLLai marundtu kuppi 'the white bottle having medicine' 'the bottle having white medicine pazaiya maaNavar viTuti 'the hostel which belongs to old student's 'the old hostel which belongs to students' In the following examples, two interpretation of meaning is possible as the word kaal has two meanings: 'one fourth' and 'leg'. avan kooziyin kaal pakutiyaic caappiTTaan 'he ate quarter part of a chicken' 'he ate the leg part of the chicken' There are a few type of lexical ambiguity: categorical ambiguity, ambiguity due to homonymy and ambiguity due to polysemy. avan meelee irukkiRaan 'He is at the top' avan meelee cenRaan 'He went up' avan meejai meelee eeiRaan 'He climbed up the hill' In the three examples given above, the meelee in the first example belongs to noun, meelee In the second example belongs to adverb and meelee in the third example belongs to postposition. So meelee is potential enough to show categorical ambiguity by three ways. In following examples, ney shows categorical ambiguity as it can denote the noun ney 'ghee' and the verb ney 'wave'. avan tuNi neytaan 'He weaved a cloth' avaL neytaan virumpukiRaaL 'She likes only ghee' In the following example, ambiguity arises due to homonymy. kuzantdai aTTaiyaip paarttu cirittatu 'The child laughed at the leech/wrapper' avan puttakattin aTTaiyaik kizittaan 'He tore the wrapper' avan aTTaiyai kampaal aTittuk konRaan 'He killed the leech with a stick' The first example can be interpreted in two ways as aTTai can mean 'leech' or 'wrapper'. in the second and third example, the ambiguity is resolved by context; in the second example, aTTai means 'wrapper' and in the third example it means 'wrapper'. Chapter 3: Machine Translation and word sense disambiguation This chapter explains word sense disambiguation in the context of machine translation. This chapter is based on "Words Sense Disambiguation: The state of Art" written by Nancy Ide and Jean Veronis, 1998). Word sense disambiguation is useful for the following applications: * Machine translation * information retrieval and hyper text navigation * content and thematic analysis * grammatical analysis * speech processing and * text processing Automated sense disambiguation was attempted in the context of machine translation. Weaver talks about the importance of sense disambiguation in his 'memorandum'. Kaplan (1950) tried to explain about sense disambiguation by an experiment. Reifler (1955) pointed out the importance of context in terms of "semantic coincidence". Consecutively semantic representation, interlingual device and semantic network came into vogue. 1960s relied upon AI based methods. Symbolic methods, connectionist methods and knowledge based methods were attempted. In eighties, machine readable dictionaries came into vogue. Lesk (1989) developed knowledge base for each dictionary sense. Thesauri have also been used for resolving polysemy. Yerowsky (1992) developed some methodology in this line of thinking. In the midst of 1980s, computational lexicons came into vogue. The multilingual on line lexical data bases like wordNets have been built in 1990s. But wordNet are found to be not very useful for resolving word sense ambiguity. Pustejovsky elaborated on generative lexicon. (Pistejovsky 1995 and Pustejovksy et al 1905) and pointed out the need to generate meanings form event structure, qualia structure and lexical inheritance structure. The last phase of 19th century and the beginning of 20th century saw the development of corpus based approaches. Weiss (1973) and Kelley and Stone (1975) proved that WSD can be performed by making use of corpus. Attempts were made for automatic sense-tagging. Brown et al (Brown et al 1991) and Gale et al proposed the use of parallel corpus for selecting words across languages. Methods were proposed for overcoming scarcity in the parallel corpus. Smoothing, class-based models and similarity based models were proposed. The role of context in sense selection is widely felt. The context for WSD has been manipulated specifically in two approaches: bag-of-words approach and syntactic relation approach. The contexts such as microcontext, topical context and domain specific contexts were recognized. Distance, collocation, and syntactic relations have been taken into account. The issues such as 'senses or usages' and 'enumeration or generation' have been raised. Chapter 4: Process of Word Sense Disambiguation and its development Word sense disambiguation has been described as AI-complete problem. There are approaches such as lexical sample or targeted WSD and All-words WSD. There are two types of resources, internal resources, unstructured resources, raw sources, senseannotated corpus and external resources. The external sauces consists of thesaurus, machine readable dictionaries, ontologies, collocational resources and the other sources such as wordNet, There are at least three types of approaches: supervised approaches unsupervised approaches and knowledge-based approaches. Supervised includes the following decision lists, decision trees, Naïve Bayes, neural network, example-based or instance based learning, Support Vector Machines and semi supervised WSD. The supervised method includes context clustering, word clustering, co-occurring graphs, Knowledge-based approaches includes over lapping of sense definition and graph based approaches. The other approaches includes determining word sense dominance, domain-driven disambiguation, and WSD from Cross-Lingual Evidence, The uses of WSD includes information retrieval (IR), information extraction, machine translation, content analysis, word processing, lexicography and semantic web. The problem includes semantic representation, knowledge acquisition and domain-driven disambiguation. Chapter 5: Word sense disambiguation processing in Tamil To start with Tamil lexical semantic is discussed in details. Different types of lexical relations such as synonymy, hyponymy, compatibility, incompatibility, meronymy and antonymy are discussed with suitable examples. The vocabulary of Tamil is structured hierarchically by taxonomy and meronymy relations. The structures could be branching or non-branching. There are few attempts for word sense disambiguation in Tamil. The first attempt of WSD in Tamil has been made by Arun. This is followed by Baskaran's research on word sense disambiguation (Baskaran 2002). He has made use of cluster approach complemented by case information. In this work, disambiguation is being performed based on the clustering technique and by using the collocations and case-markers as knowledge sources. The approach works in two phases: trainging phase and testing phase. At the beginning of the training phase, the system identifies the ambiguous words in the text using the list of ambiguous words. For each occurrence of an ambiguous word a context is identified from the text. Different researchers have used different sizes of contexts, i.e. different window sizes. The window size influences the performance of the system considerably. A larger window size brings in more unrelated words to the context, while a smaller window size misses some important collocations. Following the general practice as found in many WSD works including Yarowsky (1995), a twenty-word window is used in this work. The frequent words also called stop-words are removed from the window. The context is then morphologically analysed to get the root form of the contextual words, which are used, in clustering. Each context is represented as a context or co-occurrence vector. Using the K-means approach these vectors are collected into different clusters. Collocations are then collected from these clusters automatically and senses are assigned to these collocations by humanannotators thereby developing a sense collocation dictionary. Case-markers are also identified for each cluster and added to the sense-collocation dictionary to build a sense-collocation case-marker dictionary. In the second phase, the system is ready for disambiguation. Any text containing an ambiguous word can be given to the system and the system makes use of the sense-collocation case-marker dictionary to disambiguate the unseen occurrences of the ambiguous word. It should be noted that the training is a one-time task for each ambiguous word and once the system is trained for a particular word it can be used to resolve ambiguity of that word in a given text. It can be observed that collocations and case-markers play an important role in this method. A methodology of making use of conceptual graph for WSD has been explained next to it. This is followed by making use of Rajendran's computational Thesaurus for Tamil (2006) for WSD. Chapter 6: Conclusion The last chapter is "Conclusion". It discusses briefly about the information discussed in the last five chapters. It also discusses about the contribution made by the research work. [ABSTRACT FROM AUTHOR]

This case study survey investigates the Technological Pedagogical Contextual Knowledge (TPCK) of Science Teacher Candidates (STC) across different years of study in the Topic of Renewable Energy (TRE). In line with the transformative model, the study treats TRE-TPCK as a new knowledge domain emerging from the interaction between content, technology, pedagogy and contextual knowledge. Participants were 36 Science Education students who were in their 1st, 2nd, 3rd and 4th years of study during the 2014–2015 academic year. Teacher candidates' TPCK was identified through semi-structured interviews based on (1) Vignettes, (2) Anecdotes from a teacher named Zeynep and the (3) Content Presentation Technique (CPT). The data were analyzed by using the content analysis technique. The findings showed that the TRE-TPCK of teacher candidates across different years of study was at a similar and less-than-ideal level. STCs in different years of study mostly disregarded the macro and meso contexts when teaching the topic of renewable energy and were partially competent in the sub-meso, micro and sub-micro contexts. They particularly lacked technology-integrated evaluation knowledge when teaching TRE. [ABSTRACT FROM AUTHOR]

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