Next Words Prediction and Sentence Completion in Bangla Language Using GRU-Based RNN on N-Gram Language Model

We use a lot of devices in our daily life to communicate with others. In this modern world, people use email, Facebook, Twitter, and many other social network sites for exchanging information. People lose their valuable time misspelling and retyping, and some people are not happy to type large sentences because they face unnecessary words or grammatical issues. So, for this reason, word predictive systems help to exchange textual information more quickly, easier, and comfortably for all people. These systems predict the next most probable words and give users to choose of the needed word from these suggested words. Word prediction can help the writer by predicting the next word and helping complete the sentence correctly. This research aims to forecast the most suitable next word to complete a sentence for any given context. In this research, we have worked on the Bangla language. We have presented a process that can expect the next maximum probable and proper words and suggest a complete sentence using predicted words. In this research, GRU-based RNN has been used on the N-gram dataset to develop the proposed model. We collected a large dataset using multiple sources in the Bangla language and also compared it to the other approaches that have been used such as LSTM, and Naive Bayes. But this suggested approach provides excellent exactness than others. Here, the Unigram model provides 88.22%, Bi-gram model is 99.24%, Tri-gram model is 97.69%, and 4-gram and 5-gram models provide 99.43% and 99.78% on average accurateness. We think that our proposed method profound impression on Bangla search engines.

Naxi-English Bilingual Word Alignment Based on Language Characteristics and Log-Linear Model

We propose a method that can achieve the Naxi-English bilingual word automatic alignment based on a log-linear model.This method defines the different Naxi-English structural feature functions,which are English-Naxi interval switching function and Naxi-English bilingual word position transformation function.With the manually labeled Naxi-English words alignment corpus,the parameters of the model are trained by using the minimum error,thus Naxi-English bilingual word alignment is achieved automatically.Experiments are conducted with IBM Model 3 as a benchmark,and the Naxi language constraints are introduced.The final experiment results show that the proposed alignment method achieves very good results:the introduction of the language characteristic function can effectively improve the accuracy of the Naxi-English Bilingual Word Alignment.

How Many Words Does A Learner Need to Know in A Foreign Language

Vocabulary learning is of great importance in the successful development of communication and literacy skills(Teresa A. Belisle, 2011). Some questions will be discussed as how many words a learner needs to know? How many words are there in English? And how many words do native speakers know. A clearer idea will be presented to show the number of words different people know and need for various purposes in the daily life.

Word Embeddings and Semantic Spaces in Natural Language Processing

One of the critical hurdles, and breakthroughs, in the field of Natural Language Processing (NLP) in the last two decades has been the development of techniques for text representation that solves the so-called curse of dimensionality, a problem which plagues NLP in general given that the feature set for learning starts as a function of the size of the language in question, upwards of hundreds of thousands of terms typically. As such, much of the research and development in NLP in the last two decades has been in finding and optimizing solutions to this problem, to feature selection in NLP effectively. This paper looks at the development of these various techniques, leveraging a variety of statistical methods which rest on linguistic theories that were advanced in the middle of the last century, namely the distributional hypothesis which suggests that words that are found in similar contexts generally have similar meanings. In this survey paper we look at the development of some of the most popular of these techniques from a mathematical as well as data structure perspective, from Latent Semantic Analysis to Vector Space Models to their more modern variants which are typically referred to as word embeddings. In this review of algoriths such as Word2Vec, GloVe, ELMo and BERT, we explore the idea of semantic spaces more generally beyond applicability to NLP.

Language characteristics of cosmetic advertising

In cosmetic advertising,advertisers try their best to solve people's problems that puzzle them and to meet people's pursuit of beauty by carefully choosing those appropriate evaluative words,which will be analyzed in the present research.

Study on Characteristics of Finnish Language

The Finnish language is a member of the Finno-Ugrian language family, which is a part of the Uralian languages. Finnish is mainly spoken by five million people. Finnish language is a unique language compared to other European language. It has fifteen different cases, and the inflections occur to many aspects of Finnish grammar. And the free word order is also one of features of Finnish language. The essay focuses on the characteristics of Finnish language in the perspective of inflections and word order.

"Gelivable" "Yumi"——A Discussion of Emerging New Words in Chinese Language

Along with the unceasingly booming of new terms in Chinese language, some people complain that it is becoming more and more difficult to understand some terms like "囧", "给力","织围脖". Recently, there is a national notice about the forbidding of using"神马","PK"and "浮云" in the composition of Chinese Subject while attending the University Entrance Examination. Some linguists are wondering shall the orthodox language absorb all of them. Should the local legislation do anything about the protection of language and characters? How long will these emerging new words lively between people's lips? This article discusses the above questions by focusing on the historical development of the term "玉米", presenting the different attitudes of common people, linguists and the government, and analyzing some common social understanding of these terms.

Teachers’Characteristics and Teaching Styles as Determinants of Students’Achievement in Senior Secondary Schools Civics and English Language in Osun State,Nigeria

The study examined the status of senior secondary schools’students’achievement in Civic Education and English Language in Osun State.It also investigated the Civic Education and English Language teachers’characteristics in the state and determined the teaching styles of Civic Education and English Language teachers in the study areas and finally assessed the influence of teachers’characteristics and teaching styles on students’achievement in secondary schools Civic Education and English Language.The study adopted Discriminant Analysis.The study sample consisted of 600 senior secondary school students,30 Civics teachers,and 30 English Language teachers that were selected using multi-stage sampling procedure.Four instruments were constructed and validated before use.Three research questions were asked and answered while one hypothesis was tested and verified.The results among others showed that more than 50%of the students that participated in the study had high status of achievement in Civic Education while more than 40%of English Language students that participated in the study had low status of achievement.The study also showed that teachers’characteristics pertinent to both Civic Education teachers and English Language teachers were,effective discipline skills(Civics,M=1.20,SD=0.407;English,M=1.40,SD=0.498)and good communication with parents(Civics,M=1.43,SD=0.504;English,M=1.40,SD=0.498).In addition,there was significant influence of teachers’characteristics and teaching styles on students’achievement in Civics and English Language in senior secondary schools(X^2(2)=16.37,p<0.05).The study concluded that teachers’characteristics and teaching styles are major determinants of students’achievement in Civic Education and English Language.

New Words Keep Emerging in Chinese Language

It is generally acknowledged that language-change is universal,continuous,and,to very considerable extent,regular.The universality and continuity of the process of language-change is noted greatly.All living languages are subject to it.The core of language-change is lexical,and a language ' s vocabulary change proves to be more revolutionary for the language ' s slow evolution.

Spreading the Word A survey of popular Chinese words shows a growing awareness of the language in foreign countries

Chinese pinyin, the commonly used system for Romanizing standard Chinese, is a special form of the language. Compared with Chinese characters, pin,in boasts an advantage in the process of spreading Chinese culture around the world.

Translation of English Language into Urdu Language Using LSTM Model

English to Urdu machine translation is still in its beginning and lacks simple translation methods to provide motivating and adequate English to Urdu translation.In order tomake knowledge available to the masses,there should be mechanisms and tools in place to make things understandable by translating from source language to target language in an automated fashion.Machine translation has achieved this goal with encouraging results.When decoding the source text into the target language,the translator checks all the characteristics of the text.To achieve machine translation,rule-based,computational,hybrid and neural machine translation approaches have been proposed to automate the work.In this research work,a neural machine translation approach is employed to translate English text into Urdu.Long Short Term Short Model(LSTM)Encoder Decoder is used to translate English to Urdu.The various steps required to perform translation tasks include preprocessing,tokenization,grammar and sentence structure analysis,word embeddings,training data preparation,encoder-decoder models,and output text generation.The results show that the model used in the research work shows better performance in translation.The results were evaluated using bilingual research metrics and showed that the test and training data yielded the highest score sequences with an effective length of ten(10).

Promoting the International Dissemination of Chinese Culture Through International Chinese Language Education: A Case Study of Chinese-English Idiomatic Equivalence

One major task of our improvement of China’s international communication capabilities is to tell China’s stories well and make China’s voice heard to communicate a comprehensive,multi-dimensional view of China in the international arena.International Chinese language education bears the dual responsibility of spreading the Chinese language and promoting the Chinese culture.How to shape global narratives for telling China’s stories and improve China’s international communication capabilities has become an important issue that concerns international Chinese language education.Chinese idioms,which are hidden treasures of the Chinese language,can play a unique role in this regard.It is therefore necessary to review the content and methods of including idioms in international Chinese language education.In this paper,instruction in idioms is approached by making a corpus-based comparative analysis from the theoretical perspectives of equivalence,cognitive metaphor,and second language acquisition.Qualitative and quantitative methods are combined to analyze idioms to be added to the vocabulary of international Chinese language education in three dimensions:word frequency,semantic transparency,and functional equivalence.This paper aims to explore a new approach to the dissemination of Chinese culture through instruction in idioms in international Chinese language education.

Short-Term Memory Capacity across Time and Language Estimated from Ancient and Modern Literary Texts. Study-Case: New Testament Translations

We study the short-term memory capacity of ancient readers of the original New Testament written in Greek, of its translations to Latin and to modern languages. To model it, we consider the number of words between any two contiguous interpunctions Ip, because this parameter can model how the human mind memorizes “chunks” of information. Since IP can be calculated for any alphabetical text, we can perform experiments—otherwise impossible— with ancient readers by studying the literary works they used to read. The “experiments” compare the IP of texts of a language/translation to those of another language/translation by measuring the minimum average probability of finding joint readers (those who can read both texts because of similar short-term memory capacity) and by defining an “overlap index”. We also define the population of universal readers, people who can read any New Testament text in any language. Future work is vast, with many research tracks, because alphabetical literatures are very large and allow many experiments, such as comparing authors, translations or even texts written by artificial intelligence tools.

Vari-gram language model based on word clustering

Category-based statistic language model is an important method to solve the problem of sparse data.But there are two bottlenecks:1) The problem of word clustering.It is hard to find a suitable clustering method with good performance and less computation.2) Class-based method always loses the prediction ability to adapt the text in different domains.In order to solve above problems,a definition of word similarity by utilizing mutual information was presented.Based on word similarity,the definition of word set similarity was given.Experiments show that word clustering algorithm based on similarity is better than conventional greedy clustering method in speed and performance,and the perplexity is reduced from 283 to 218.At the same time,an absolute weighted difference method was presented and was used to construct vari-gram language model which has good prediction ability.The perplexity of vari-gram model is reduced from 234.65 to 219.14 on Chinese corpora,and is reduced from 195.56 to 184.25 on English corpora compared with category-based model.

Research on Cross-Language Retrieval Using Bilingual Word Vectors in Different Languages

Bilingual word vectors have been exploited a lot in cross-language information retrieval research. However, most of the research is currently focused on similar language pairs. There are very few studies exploring the impact of using bilingual word vectors for cross-language information retrieval in long-distance language pairs. In this paper, it systematically analyzes the retrieval performance of various European languages (English, German, Italian, French, Finnish, Dutch) as well as Asian languages (Chinese, Japanese) in the adhoc task of CLEF 2002–2003 campaign. Genetic proximity was used to visually represent the relationships between languages and compare their crosslingual retrieval performance in various settings. The results show that the differences in language vocabulary would dramatically affect the retrieval performance. At the same time, the term by term translation retrieval method performs slightly better than the simple vector addition retrieval methods. It proves that the translation-based retrieval model can still maintain its advantage under the new semantic scheme.

Chinese New-coined Words Management in P.R.C

The paper relies on the language management model. First of all, the thousands of new words coined each year in China can be roughly divided into eight groups. Besides, it exemplifies the problems that have occurred in communication. Last but not least, to reflect language management acts/processes, it reports the efforts the language authorities and governmental bodies have made in the struggle to solve the problems. It attempts to remind the language management agencies of the point that for the use of new words, the language users are always right as long as communication flows without problems in understanding.

The Deterioration of the English Language

The English language has been deteriorating because of the modern e-mail and chat room language abbreviations,in corporation of different dialects depending on culture,and the inappropriate use of words as well as incorrect spelling.If we want to keep English language from deterioration,we should take advantage of the advances in technology and to learn about other cultures dialects,but we should keep the proper written and spoken English in mind.

A study on the Popular Words on Line——Take the Popular Words of Recent Years as Examples

Popular word on line is a kind of language form which is welcomed by netizens.The popular word on line is the most active part of network language.They are made of expressions what the internet users are established by usage.The popular words on line have a very strong nature of medium.They are the comprehensive products which come from the facts of social politics,economics,culture,environment and people's psychological activities etc.of a certain time.The total amount of new emerging popular words on line is not so many every year,but the influence of them can not be belittled.This study focuses on the popular words on line with the cases of the popular words occured in recent years.It works on the reasons for their rapid spreading.The author summarizes their characteristics and put forward some ways for the guidance of the popular words on line.