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 valuab...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.展开更多
In social anthropology,"culture"is a catch word for all those patterns of thinking, feeling, and acting. It is always a collective phenomenon, because it is at least partly shared with people who live or liv...In social anthropology,"culture"is a catch word for all those patterns of thinking, feeling, and acting. It is always a collective phenomenon, because it is at least partly shared with people who live or lived within the same social environment. Culture can be classified into several layers of which historical, regional, religious, and social cultures are discussed in this paper. Language and culture are closely related. Certain language reflects certain culture in which the language is used. As two different languages, Chinese and English have their own cultural characteristics and connotations. This paper compares the cultural connotations of Chinese and English color words to see how important the cultural background of a language is in cross-cultural communication.展开更多
Bible has a lot of Chinese versions, among which The Chinese Union Version(CUV) and Today's Chinese Version(TCV) are most popular. The skopos of CUV is for Chinese Christians while the skopos of TCV is not just fo...Bible has a lot of Chinese versions, among which The Chinese Union Version(CUV) and Today's Chinese Version(TCV) are most popular. The skopos of CUV is for Chinese Christians while the skopos of TCV is not just for Chinese Christians but also for the non-believers. Different target readers decide different skopos of translation. The comparison of CUV and TCV of this essay will focus on the word choices and sentence patterns to illustrate the point. Concerning the word choice, CUV uses a lot of classical words that are hard for readers today to understand. As to the sentence pattern, CUV version is generally literal translation to be loyal to God. On the contrary, because of different skopos, TCV version is very free translation. We can not deny the fact that TCV using free translation is under the influence of Nida whose dynamic equivalence is influential during the1970s especially in the Bible translation. However, from the prospective of Vermeer's theory, the prime skopos of TCV is to help non-believers understand and be willing to read Bible. Strange sentences and vague meanings will prevent them from go on reading it. Since I am also a Christian, at the end of the essay I will illustrate the limitation of both versions from a Christian's perspective.展开更多
English and Chinese are different.Generally speaking,English is referred to as static language while Chinese dynamic.This paper tries to compare the static
Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summariza...Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summarization is described. Three kernel descriptors such as gradient, shape, and color are extracted, respectively. Feature late-fusion is executed in turn by the multiple kernel learning model to obtain more discriminant image features. Absolute rank and relative rank of the tag-rank model are used to boost the key words＇ weights. A new word integration algorithm named word sequence blocks building （WSBB） is designed to create N-gram word sequences. Sentences are generated according to the N-gram word sequences and predefined templates. Experimental results show that both the BLEU-1 scores and BLEU-2 scores of the sentences are superior to those of the state-of-art baselines.展开更多
文摘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.
文摘In social anthropology,"culture"is a catch word for all those patterns of thinking, feeling, and acting. It is always a collective phenomenon, because it is at least partly shared with people who live or lived within the same social environment. Culture can be classified into several layers of which historical, regional, religious, and social cultures are discussed in this paper. Language and culture are closely related. Certain language reflects certain culture in which the language is used. As two different languages, Chinese and English have their own cultural characteristics and connotations. This paper compares the cultural connotations of Chinese and English color words to see how important the cultural background of a language is in cross-cultural communication.
文摘Bible has a lot of Chinese versions, among which The Chinese Union Version(CUV) and Today's Chinese Version(TCV) are most popular. The skopos of CUV is for Chinese Christians while the skopos of TCV is not just for Chinese Christians but also for the non-believers. Different target readers decide different skopos of translation. The comparison of CUV and TCV of this essay will focus on the word choices and sentence patterns to illustrate the point. Concerning the word choice, CUV uses a lot of classical words that are hard for readers today to understand. As to the sentence pattern, CUV version is generally literal translation to be loyal to God. On the contrary, because of different skopos, TCV version is very free translation. We can not deny the fact that TCV using free translation is under the influence of Nida whose dynamic equivalence is influential during the1970s especially in the Bible translation. However, from the prospective of Vermeer's theory, the prime skopos of TCV is to help non-believers understand and be willing to read Bible. Strange sentences and vague meanings will prevent them from go on reading it. Since I am also a Christian, at the end of the essay I will illustrate the limitation of both versions from a Christian's perspective.
文摘English and Chinese are different.Generally speaking,English is referred to as static language while Chinese dynamic.This paper tries to compare the static
基金The National Natural Science Foundation of China(No.61133012)the Humanity and Social Science Foundation of the Ministry of Education(No.12YJCZH274)+1 种基金the Humanity and Social Science Foundation of Jiangxi Province(No.XW1502,TQ1503)the Science and Technology Project of Jiangxi Science and Technology Department(No.20121BBG70050,20142BBG70011)
文摘Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summarization is described. Three kernel descriptors such as gradient, shape, and color are extracted, respectively. Feature late-fusion is executed in turn by the multiple kernel learning model to obtain more discriminant image features. Absolute rank and relative rank of the tag-rank model are used to boost the key words＇ weights. A new word integration algorithm named word sequence blocks building （WSBB） is designed to create N-gram word sequences. Sentences are generated according to the N-gram word sequences and predefined templates. Experimental results show that both the BLEU-1 scores and BLEU-2 scores of the sentences are superior to those of the state-of-art baselines.
