Review Meaning In Bengali With Example

Translation is an endeavour to automate all or part of the process of translation from one language to another. In general, translation is a complex task, which aims at preserving the semantic and stylistic equivalents of the source language texts into the target language texts. The most problematic area in manual and machine translation is the lexicon and the role it plays according to the context to create deviations. There are also cases where deviations occur owing to the divergence – one of the complex areas of investigation in translation. Divergence in translation normally arises when the sentences in the source language are realized differently in the target language.

This paper seeks to discuss some of the major divergences that observed in English to Bengali translation. It also investigates how the different linguistic and extralinguistic constraints can play decisive roles in translation, resulting in divergences and other issues. The primary objective of this paper is to understand the types of divergence problems that operate behind English to Bengali translation the study of which is still in a state of its infancy. Proper identification and understanding of these problems are important in both manual and machine translation. Moreover, resolution of these problems is a pre-requisite for designing a robust machine translation system between the languages considered for the present study. Sentiment analysis has become a key research area in natural language processing due to its wide range of practical applications that include opinion mining, emotions extraction, trends predictions in social media, etc.

Though the sentiment analysis in English language has been extensively studied in recent years, a little research has been done in the context of Bangla language, one of the most spoken languages in the world. In this paper, we present a comprehensive set of techniques to identify sentiment and extract emotions from Bangla texts. We build deep learning based models to classify a Bangla sentence with a three-class and a five-class sentiment label. We also build models to extract the emotion of a Bangla sentence as any one of the six basic emotions . We evaluate the performance of our model using a new dataset of Bangla, English and Romanized Bangla comments from different types of YouTube videos.

Our proposed approach shows 65.97% and 54.24% accuracy in three and five labels sentiment, respectively. We also show that the performance of our model is better for domain and language specific texts. Transition between primary schools and secondary schools is reported to be inconsistent. In 2020, the survey report 'Language trends' noted that 46% of respondents from state secondaries said they had no contact with primary schools with regard to languages. In addition, 74% reported that they receive no data on pupils' prior attainment.

Almost 70% of respondents state that in key stage 3, some pupils start a different language than what they studied at primary level. Just 4% of secondary teachers say that all pupils in Year 7 continue with the same language learned at primary school. Indeed, the report states that 'more often than not, language learning at key stage 3 starts from scratch'.

There are other barriers that potentially stop languages flourishing in England. Pupils who take part in exchange programmes sometimes feel demotivated when they compare their own linguistic ability with that of their peers abroad. Multiple studies show that pupils learning English abroad begin to do so between the ages of 6 and 8. They spend more time on languages and most continue studying languages until the end of compulsory education.

For example, compared with the EU average, the amount of time spent by English pupils learning languages was an hour a week less for the first foreign language and 2 hours a week less for the second foreign language. Pupils' levels of attainment are clearly based on more than simply their number of hours spent studying languages, however. Midline shift of the brain is an important feature that can be measured using various imaging modalities including X-ray, ultrasound, computed tomography, and magnetic resonance imaging.

Shift of midline intracranial structures helps diagnosing intracranial lesions, especially traumatic brain injury, stroke, brain tumor, and abscess. Being a sign of increased intracranial pressure, MLS is also an indicator of reduced brain perfusion caused by an intracranial mass or mass effect. We review studies that used the MLS to predict outcomes of patients with intracranial mass. In some studies, the MLS was also correlated to clinical features.

Automated MLS measurement algorithms have significant potentials for assisting human experts in evaluating brain images. In symmetry-based algorithms, the deformed midline is detected and its distance from the ideal midline taken as the MLS. In landmark-based ones, MLS was measured following identification of specific anatomical landmarks. To validate these algorithms, measurements using these algorithms were compared to MLS measurements made by human experts.

Even though we know that the generation of BTC is an essential part of MT, we have not tried to understand how these BTC are going to be used in the work. In our view, an MT system will be more robust if it is powered with linguistic resources developed from linguistic tasks carried out on BTC. The activities we propose here are not only suitable for MT from English to Bangla, but also for any two language pair.

These are also applicable for most of the Indian languages engaged in developing BTC between English and the Indian languages. The linguistic resources generated from analysis of BTC can also be used in language teaching, electronic dictionary compilation, machine learning, grammar development, and language cognition. With the application of some translation support tools and structured knowledge resources available to the translators, they have translated more than sentences from Hindi to Bangla.

This paper presents some of the problems and challenges the translators have faced as well as the strategies they have applied to overcome these challenges. Computational study of identifying and extracting sentiment content of textual data that can be used to classify those public opinions posted on various topics in social media. Comparative result analysis is also provided between classifiers, where LR performed slightly better than SVM and NB by considering n-gram as a feature with an accuracy of 83%. The process of assessing the emotional tone behind a document in order to comprehend the expressed opinions, views and emotions is known as sentiment analysis.

We preprocessed our initial raw data through several steps and applied the TF-IDF technique for feature extraction. We have shown a comparative analysis of the classifiers used in sentiment detection. In our study, deep learning approaches have shown better performance than classical approaches with an accuracy of 96.95% by LSTM. Among the classical approaches, Support vector machine and Random forest classifier have achieved maximum accuracy of 78.23% and 78.37%, respectively.

The combination of machine learning approach and natural language processing is applied to analyze the sentiment of text for particular sentences. Restaurant business was always a popular business in Bangladesh. These business is now Leaning towards online delivery services and the overall quality of restaurants are now judged by reviews of customers. One try to understand the quality of a restaurant by the reviews from other customers. These opinions of customers organizing in structured way and to understand perception of customers reviews and reactions is the main motto of our work.

Collecting data was the first thing we have done for deploying this piece of work. Then making a dataset which we harvested from websites and tried to deploy with deep learning technique. In this piece of research, a combined CNN-LSTM architecture used in our dataset and got an accuracy of 94.22%. Also used some other performance metrics to evaluate our model. Opinion mining is the computational study of people's opinions, emotions and attitudes which is one of the key research field in Natural Language Processing .

To cope with the competitive world, owners of business need to extract exact opinion of people about his/her business. Recently, people in Bangladesh are more interested to express their opinion in Bangla and most importantly in Phonetic Bangla rather than English. Our approach starts by preprocessing raw data and then feature extraction with different N-gram techniques. Then vectorization is applied on that data with HashingVectorizer, CountVectorizer and TF-IDF vectorizer.

Later machine learning based approaches namely Support Vector Machine , Decision Tree and Logistic Regression are applied to classify reviews. We have classified the reviews in three different classes, i.e. bad, good and excellent. Finally a comparison is shown between vectorizers in accordance with different classifiers where SVM provides better accuracy with 75.58%. More importantly, learners' use of the target language should be considered central to pedagogy. For learners to create the meaning they want , they need both the linguistic capability and the motivation for 'real' speech. The classroom should enable pupils to try out the target language.

It should help them consolidate their knowledge, while the teacher provides examples of, and monitors, language use. It is important to remember that speed and accuracy often decrease when speech is produced spontaneously. This section highlights the pressured position that languages are in. It discusses the main challenges that we face, including the decrease in uptake of languages over the years. It notes that, although languages as a subject is pressured, it is also pivotal to the success of the national English Baccalaureate ambition.

The proportion of boys, disadvantaged pupils and those with special educational needs and/or disabilities engaging in languages after key stage 3 is low. Staff expertise, curriculum planning, time allocation and transition are cited as barriers at key stage 2. Pupils' motivation is discussed as a focus to help languages to flourish. This includes the need for pupils to feel successful in their learning and that they are clear about their next steps. The issue of translation divergence is an important research topic in the area of machine translation.

An exhaustive study of the divergence issues in MT is necessary for their proper classification and resolution. In the literature on MT, scholars have examined the issue and have proposed ways for their classification and resolution . However, the topic still needs further exploration to identify different sources of translation divergence in different pairs of translation languages. In this paper, we discuss translation patterns between Hindi and English of different types of constructions with a view to identifying the potential topics of the translation divergences. We take Dorr's classification of translation divergence as the base to examine the different topics of translation divergence in Hindi and English.

The primary goal of the paper is to point out different types of translation divergences in Hindi and English MT that have not been discussed in the existing literature. The existence of translation divergence precludes straightforward mapping in the MT system. An increase in the number of divergences also increases the complexity, especially in linguistically motivated transfer-based MT systems.

In addition, handling strategies of different types of divergences in a transfer-based approach to MT are discussed. The paper also includes the evaluation method and how an improvization takes place with the application of DI in MT. Sentiment analysis is one of the important fields of Machine learning Language which involves analysis using natural language processing. The main goal of sentiment analysis is to detect and analyze attitude, opinions or sentiments in the text.

Sentiment analysis has reach edits popularity by extracting Knowledge from huge amount data present online. The Process of analysis includes selecting features and opinion which is a challenging task in languages other than English. There are very few research works done for determining sentiments in regional languages.

The manual approach to analyze this textual reviews is complex and time-consuming and hence it requires specialized automated systems. If leaders teach fixed phrases initially, they must ensure that they also teach pupils to manipulate the words and grammar they contain, as soon as sensible. Generally, only a very few highly frequent and useful phrases should be taught without helping learners to manipulate their component parts.

The present paper attempts at exploring, classifying and resolving various types of divergence patterns in the context of English-Urdu Machine Translation where English and Urdu are SL and TL respectively. So far as the methodology is concerned, we have observed the English-Urdu pair sentences and analyzed the translated output taking into consideration different areas of translational divergences. Dorr's theoretical framework has been adopted for the classification and resolution of the linguistic divergences in this undertaken study. Dorr has classified the divergences into two broad categories (lexical-semantic and syntactic divergences) and proposed Lexical Conceptual Structure-based resolution for them. This study would help identify, classify and resolve the underlying divergence patterns between these languages so as to develop MT systems considering the divergence errors and enhance the performance of the MT. Of course, we want learners to be exposed to the language they are learning.

However, we do not want them to be overwhelmed by it in their early stages of language learning to the point that it could demotivate them. The use of the target language by the teacher should not hinder pupils from being able to develop an understanding of the structure of the language. At the same time, using the target language is an essential part of practice and reinforcement, including building familiarity with rhythms, sounds and intonation. With time and practice, knowledge of phonics, grammar and vocabulary becomes automatised.

With this, learners can understand longer written texts and spoken discourse. In turn, this means that they can access a wider range of meanings across a range of contexts and purposes of language use. They will also be more likely to efficiently and appropriately draw on contextual information, for example other words in the discourse. They can also bring in their knowledge of the world or background knowledge of a topic.

This all allows them to better understand both familiar and less familiar topics and further develop their understanding of the culture of the language in scope. The goals of having pupils broaden their horizons, converse fluently with others, fully explore cultures and strengthen their economic prospects can only be reached if we build firm foundations of language learning. Only by mastering the basics can pupils engage fully in the process of language learning, which they can then use to communicate about an increasingly wide range of themes.

With increasing linguistic ability, cultural awareness can become ever more refined. To improve learners' understanding and production of language, a steady development in understanding of phonics, vocabulary, grammar and their interplay is needed. This review explores the literature relating to the field of foreign languages education. Its purpose is to identify factors that contribute to high-quality school languages curriculums, assessment, pedagogy and systems.

We will use this understanding of subject quality to examine how languages are taught in England's schools. Midline shift is a well-proven composite imaging sign that can be measured on CT, MRI, and US. Standardization of MLS measurement facilitates communication and comparison between different raters and permits further automation. We have summarized current state of the art in MLS measurement and its relationship to other clinical and imaging parameters. Characteristics, limitations, and validation of automated algorithms that help measuring MLS were reviewed. We have also highlighted novel imaging parameters or their combinations that may lead to a better understanding of brain displacement and deformation as well as their clinical implications.