Vietnamese Sentences Research Articles

The Semantics of Stative Locative Events in Vietnamese: A Spatial Exploration

This study utilizes mathematical formalism and Figure-Ground argument mapping to categorize stative locative events in Vietnamese as surjective or bijective. Bijective events exhibit a one-to-one mapping of Figures and Grounds, while surjectives allow a single Figure to map onto multiple Grounds. Through analysis of Vietnamese sentences, the paper demonstrates how bijective and surjective locatives encode deterministic and ambiguous spatial relations between Figures and Grounds, respectively. This novel distinction aids in enhancing the understanding of locative event typologies and provides an explanatory model for cross-linguistic analysis. The proposed integrated formal framework combines predicate logic, set theory, and lexical semantics to account for complex locative meanings, with significant applications in computational and theoretical linguistics, as well as cognitive science.

Analysis Methods of Vietnamese Sentence and Culture in Vietnamese Sentences

In this article, the researchers will apply Shen Xiaolong's (申小龙) theory of sentence culture and combine accumlated knowledge to analyze Vietnamese sentences with the function of expressing and commenting. The results indicate that using SVO structure to analyze Vietnamese sentences has many disadvantages and no longer appropriate. Hence, it must come from the fact that the characteristics of Vietnamese sentences themselves, based on culture and ideology of Vietnamese people, not from any other nation. The article will introduce a new analysis method on Vietnamese sentences and culture in Vietnamese sentences. We applied Shen Xiaolong's （申小龙）theory of sentence cultureand combined Vietnamese grammar and accumulated knowledge to analyze Vietnamese sentences. The results show that using Shen Xiaolong's theory of sentence culture to analyze performance sentences and topic - comment sentences in is completely appropriate. However, within the scope of this article, we have only applied that theoretical framework to analyze the above two types of sentences, but have not tried it with all types of sentences.

Empty Locative and Temporal Expressions in Vietnamese Sentences and Their Role in Conveying Meaning

Empty Locative and Temporal Expressions in Vietnamese Sentences and Their Role in Conveying Meaning

Translating Vietnamese Sentences Containing the Word Rồi into English Related to English Tenses

In Vietnamese, rồi can help translators choose appropriate English tenses to translate Vietnamese sentences into English. With this study, the researcher wants to indicate the influence of rồi in four Vietnamese works’ sentences on their translators’ selection for appropriate English tenses to have semantically equivalent translations. The study refers to six tenses of simple present (a), simple past (b), present perfect (c), past perfect (d), simple future (e), and near future (f). These sentences were translated into English with (a) if rồi denotes the emphasis on the reality of the event. (b), (c), and (d) are preferred when rồi implies the event that happened in the past. However, using (e) and (f) is not dependent upon the occurrence of rồi because this word does need combination with other words or phrases to denote the meaning of the future. Therefore, understanding the influence of rồi in Vietnamese in different contexts helps Vietnamese people learning English know how to choose the appropriate English tenses when translating Vietnamese sentences containing it into English.

Future meaning in Vietnamese and English: Similarities and Differences

The paper describes in detail and employs one table and six discussions to compare expressions of future meaning in Vietnamese and English declaratives to find out their differences and similarities. The findings are the basis for giving some advice to learners of Vietnamese and English. Vietnamese neither distinguishes nor employs grammatical means to express various shades of future meaning. A lexical means like sẽ, định, tính, or sắp, or a combination of two or more like định sẽ, tính sẽ, or dự tính sẽ, does. The lexical means may be omitted when an adverbial of future time like sáng mai, meaning tomorrow morning, occurs. At first glance, Vietnamese learners face difficulty because one expression in their mother tongue separates into two or more in English, resulting in unnecessary differentiation; conversely, native speakers of English seem more enjoyable noticing that two or more expressions in their mother tongue merge into one in Vietnamese. However, to understand Vietnamese sentences, foreigners must depend more on contextual cues than when they process English sentences. This is uneasy for the native English speakers, who are accustomed to using a language in which all the modal meanings have signs, either lexical or grammatical or both, with an explicit indicator in the structure of the nuclear predication of the declaratives.

Strategies for Translating English Passive Sentences into Vietnamese

This study examines some most common strategies for the Vietnamese translation of English passive voice such as “The use of markers ‘được’/‘bị’”, “Change to active”, “Paraphrasing” in order to find out the frequency and the rules for the use of those strategies by collecting and analyzing the data from the English and Vietnamese versions of a novel named “Harry Potter and the Sorcerer’s Stone” and a book named “A Brief History of Time”. And the findings of this study disapproves the view that Vietnamese active sentences are the dominant choice for translating English passive voice since its results indicate that English It-clefts or English agentless passive sentences where the agent of the action is unclear or concerned with generalization rather than specific individuals have a high tendency of being translated into active structures in Vietnamese while using a marker like 'được' or 'bị' and subject-less active sentences are the primary ways for translating other kinds of English agentless passive sentences into Vietnamese. Besides, the results of the research also indicate that the use of markers 'được'/'bị' is frequently applied to translate agent-included passives, but the position of the agent in Vietnamese sentences varies according to its features.

Improving the Performance of Vietnamese–Korean Neural Machine Translation with Contextual Embedding

With the recent evolution of deep learning, machine translation (MT) models and systems are being steadily improved. However, research on MT in low-resource languages such as Vietnamese and Korean is still very limited. In recent years, a state-of-the-art context-based embedding model introduced by Google, bidirectional encoder representations for transformers (BERT), has begun to appear in the neural MT (NMT) models in different ways to enhance the accuracy of MT systems. The BERT model for Vietnamese has been developed and significantly improved in natural language processing (NLP) tasks, such as part-of-speech (POS), named-entity recognition, dependency parsing, and natural language inference. Our research experimented with applying the Vietnamese BERT model to provide POS tagging and morphological analysis (MA) for Vietnamese sentences,, and applying word-sense disambiguation (WSD) for Korean sentences in our Vietnamese–Korean bilingual corpus. In the Vietnamese–Korean NMT system, with contextual embedding, the BERT model for Vietnamese is concurrently connected to both encoder layers and decoder layers in the NMT model. Experimental results assessed through BLEU, METEOR, and TER metrics show that contextual embedding significantly improves the quality of Vietnamese–Korean NMT.

English-Vietnamese Cross-Lingual Paraphrase Identification Using MT-DNN

Paraphrase identification is a crucial task in natural language understanding, especially in cross-language information retrieval. Nowadays, Multi-Task Deep Neural Network (MT-DNN) has become a state-of-the-art method that brings outstanding results in paraphrase identification [1]. In this paper, our proposed method based on MT-DNN [2] to detect similarities between English and Vietnamese sentences, is proposed. We changed the shared layers of the original MT-DNN from original the BERT [3] to other pre-trained multi-language models such as M-BERT [3] or XLM-R [4] so that our model could work on cross-language (in our case, English and Vietnamese) information retrieval. We also added some tasks as improvements to gain better results. As a result, we gained 2.3% and 2.5% increase in evaluated accuracy and F1. The proposed method was also implemented on other language pairs such as English – German and English – French. With those implementations, we got a 1.0%/0.7% improvement for English – German and a 0.7%/0.5% increase for English – French.

Vietnamese Sentence Paraphrase Identification using Pre-trained Model and Linguistic Knowledge

The paraphrase identification task identifies whether two text segments share the same meaning, thereby playing a crucial role in various applications, such as computer-assisted translation, question answering, machine translation, etc. Although the literature on paraphrase identification in English and other popular languages is vast and growing, the research on this topic in Vietnamese remains relatively untapped. In this paper, we propose a novel method to classify Vietnamese sentence paraphrases, which deploys both the pre-trained model to exploit the semantic context and linguistic knowledge to provide further information in the identification process. Two branches of neural networks built in the Siamese architecture are also responsible for learning the differences among the sentence representations. To evaluate the proposed method, we present experiments on two existing Vietnamese sentence paraphrase corpora. The results show that for the same corpora, our method using the PhoBERT as a feature vector yields 94.97% F1-score on the VnPara corpus and 93.49% F1-score on the VNPC corpus. They are better than the results of the Siamese LSTM method and the pre-trained models.

The Diversity of Semantic and Syntactic Properties of the Morpheme Co in Vietnamese

The present study is concerned with the diversity of the semantic and syntactic properties of the morpheme có in Vietnamese. The morpheme có possesses a variety of meanings and syntactic structures. Morpheme có may mean HAVE or THERE BE in English, and may also serve as a modal/auxiliary. The paper presents a thorough analysis of the semantic and syntactic roles of this morpheme. Data collected from 40 Vietnamese English learners of different levels were examined to determine to what extent they perceived or recognized the meanings and syntactic structures of the morpheme có while translating a number of Vietnamese sentences into English. The material consisted of 14 Vietnamese statements involving the morpheme có in different syntactic forms and semantic properties. The findings showed that more than 81% of students have no difficulties translating similar constructions such as THERE BE + NP and HAVE Possession in comparison with other constructions in which morpheme có signaled conditionality, acted as an auxiliary or was followed by an NP. Additionally, students of different proficiency levels displayed different accuracy levels in performing the translations with the morpheme có. This study provides Vietnamese language teachers, learners, and translators with an in-depth understanding of morpheme có in using different typologies of existential and possessive constructions.

The phenomenon of code switching between Vietnamese and English in communication of Vietnamese office staff

Under globalization, Vietnam has been one of the investment destinations for many foreign partners. Intercultural communication becomes an indispensable requirement for countries in general and Vietnam in particular. In Vietnam, intercultural communication can be found regularly among office staff in foreign-invested companies as well as those working for companies with relation to foreign partners. This has made a great influence on the language used. This article mentions the phenomenon of language switching among office employees in Vietnam, which happens so naturally that speakers are often unaware of it. Some English words are included in normal Vietnamese sentences, not only in spoken but also in written communication because of no Vietnamese equivalent. This trend becomes inevitable with the presence of cultural interaction and fuzziness. In addition, the article also identifies a number of not only some daily-used but also professional English words in trading. From this interesting perspective, it is expected that we would have a positive view towards the phenomenon of including English in the daily communication language among Vietnamese youth.

Diagnostic Accuracy of Sentence Repetition and Nonword Repetition for Developmental Language Disorder in Vietnamese.

Purpose Sentence repetition and nonword repetition assess different aspects of the linguistic system, but both have been proposed as potential tools to identify children with developmental language disorder (DLD). Cross-linguistic investigation of diagnostic tools for DLD contributes to an understanding of the core features of the disorder. This study evaluated the effectiveness of these tools for the Vietnamese language. Method A total of 104 kindergartners (aged 5;2-6;2 [years;months]) living in Vietnam participated, of which 94 were classified as typically developing and 10 with DLD. Vietnamese sentence repetition and nonword repetition tasks were administered and scored using multiple scoring systems. Sensitivity, specificity, and likelihood ratios were calculated to assess the ability of these tasks to identify DLD. Results All scoring systems on both tasks achieved adequate to excellent sensitivity or specificity, but not both. Binary scoring of sentence repetition achieved a perfect negative likelihood ratio, and binary scoring of nonword repetition approached a highly informative positive likelihood ratio. More detailed scoring systems for both tasks achieved moderately informative values for both negative and positive likelihood ratios. Conclusions Both sentence repetition and nonword repetition are valuable tools for identifying DLD in monolingual speakers of Vietnamese. Scoring systems that consider number of errors and are relatively simple (i.e., error scoring of sentence repetition and syllables scoring of nonword repetition) may be the most efficient and effective for identifying DLD. Further work to develop and refine these tasks can contribute to cross-linguistic knowledge of DLD as well as to clinical practice.

Chinese–Vietnamese bilingual news event summarization based on distributed graph ranking

Multi-language news event summarization aims to quickly obtain the important information from lots of related news texts written in different languages automatically. Considering that the main expressed information for a same event is similar no matter what language it is presented, the paper proposes a novel unified approach to summarize important information from the monolingual and Chinese–Vietnamese bilingual news sets simultaneously. Firstly, analyzing the sentence dependence relationship, making rules to segment sentence into different grammatical parts, a bilingual dictionary is used to set up bilingual feature space. Secondly, Chinese–Vietnamese sentence graph model is calculated distributively. Finally, using the features that graph nodes can boost each other and fusing context information, the sentences are ranked based on whether they can represent the important information. The experimental result shows that our method is effective.

Cognitive Spaces in Vietnamese Sentences

Cognitive Spaces in Vietnamese Sentences

Some Notes for Translating Vietnamese and English Sentences

The paper is about how to identify the attributes closely related to the two notions named Subject and Đề, the latter including the Topic and the Range Topic, as well as to find out how to translate the Vietnamese sentences the basic structure of which is the Topic-Comment structure into the English sentences the basic structure of which is the Subject-Predicate structure. The paper starts with a selected Vietnamese declarative which in its typical <i>Topic-Comment structure</i> with a succession of three Range Topics in the initial position in order to show the time and space in which whatever presented in the Comment occurs. The paper then presents, one after another, the two suggested translated versions in English: the former requires a Subject which doubles as Topic, which is ‘you’ in this case, in order to play the semantic role of “the Actor”; the latter is an inverted sentence in English, which is definitely equivalent in sense and better reflects the very Topic-Comment structure as well. Considerable attention has been paid to the distinction between the Topic and the Range Topic in the Vietnamese sentences in question. Such a distinction is crucial not only to widely perceiving what is called “meaning” but also to producing well-formed translated versions, either from English into Vietnamese or vice versa. After the theoretical points that are based basically on Functional Grammar are some notes for translating Vietnamese and English sentences. Hopefully, this contributes to drawing the attention of those who have practiced translating, and hopefully professional translators as well, to sophisticated issues in translation, both from and into the English language, which is part of the whole process of language learning.

Keywords extraction in Chinese–Vietnamese bilingual news based on hypergraph

The keywords extraction of bilingual news events in China and Vietnam has a very important role in understanding bilingual news events. It can quickly locate and briefly compare the news of the same events reported by the two countries. Chinese–Vietnam news texts are typically unstructured big data. How to extract the keywords that characterize the news in these unstructured data is the difficult problem of unstructured big data analysis. Bilingual documents are difficult to understand because bilingual Chinese and Vietnamese are not in the same language space. However, the hypergraph of the hypergraph model can better express the multiple relations of the vocabulary association and the entity association for bilingual news. Therefore, a method based on hypergraph for bilingual news keywords extraction is proposed. In this method, bilingual news words are extracted to construct a bilingual word set, and the words are taken as vertices. Chinese–Vietnamese sentences and bilingual words with the same semantic meaning as different types of hyperedges and the bilingual word frequency are used as the attribute to construct a bilingual news item word hypergraph model. Then, the directional diffusion algorithm in the wireless sensor network is used to iteratively calculate the weights of the vertices so as to realize the extraction of keywords in the Chinese–Vietnam bilingual news. The experimental results show that the proposed hypergraph method is better than the single-document extraction method, which can better obtain the keywords of the bilingual unstructured text data.

A Valence-Totaling Model for Vietnamese sentiment classification

Many researchers, applications and fields of study have researched and used many works concerning the sentiment classification. Each model (or method) of the sentiment analysis has many advantages and many disadvantages. Thus, we see that the opinion classification is an extremely important field of research. In this study, we have proposed a Valence-Totaling Model for Vietnamese (called VTMfV, a new model for Vietnamese sentiment classification) to classify many Vietnamese documents. First of all, we built a new Vietnamese sentiment dictionary which contains sentiment-bearing Vietnamese words such as negative Vietnamese words, positive Vietnamese words and neutral Vietnamese words. The Jaccard Measure (JM) is a similarity measure between two words (or two vectors); our Vietnamese sentiment dictionary has been created using JM. We call the Vietnamese sentiment dictionary “VSD_JM”. JM has been used in many researches of the English sentiment classification; however, it has not yet been used in any study of the Vietnamese sentient classification. From this moment, JM can be applied for the researches of the Vietnamese sentiment analysis. Then, our VTMfV has used our VSD_JM to classify the Vietnamese documents. We have processed all kinds of Vietnamese sentences. Finally, we have used the VTMfV to classify 30,000 Vietnamese documents which include the 15,000 positive Vietnamese documents and the 15,000 negative Vietnamese documents. We have achieved accuracy in 63.9% of our Vietnamese testing data set. VTMfV is not dependent on the special domain. VTMfV is also not dependent on the training data set and there is no training stage in this VTMfV. From our results in this work, our VTMfV can be applied in the different fields of the Vietnamese natural language processing. In addition, our TCMfV can be applied to many other languages such as Spanish, Korean, etc. It can also be applied to the big data set sentiment classification in Vietnamese and can classify millions of the Vietnamese documents.

Dependency Syntactic Analysis for Opinion Mining

Background/Objectives: Dependency parsing is one of the remaining problems that attracts researchers’ interest because of its contribution to natural language processing applications. This paper presents a dependency parser and provides an effective sentiment analysis model for Vietnamese. Methods/Statistical Analysis: Up to now, there has been some remarkable work on dependency parsing for Vietnamese, of which the approach of transforming the constituents into a dependency grammar has been proven to give the best performance. In this study, we proposed another way to parse Vietnamese sentences to create dependency trees based on the incremental algorithm by Covington and Graph Unification Programming. We have also combined a dependency parser, sentiment dictionary, domain ontology, and syntactic dependency rules for solving the sentiment analysis problem. Findings: We found that the experiments using simple sentences collected from Vietnamese reviews provided better results than with other methods. The combination of dependency parser, sentiment dictionary, domain ontology, and syntactic dependency rules may be helpful when dealing with a sentiment analysis problem. This model can be applied to Vietnamese and other languages. Application/Improvements: The positive results, which came from an experimental period, will provide us with a basis from which to expand grammar rules to build a parser for more complicated Vietnamese sentences and to integrate it into many cognitive tasks, such as sentiment analysis.

Xác định thứ tự thời gian giữa hai câu tiếng Việt chỉ quá trình để tóm lược

In this paper we introduce a method for summarizing the meaning of two continual Vietnamese sentences manifesting a sequence of processes which belongs to one of three process types (according to Functional Grammar [26, 41]): the state of subject is changed, the position of subject is changed, and the state or position of the subject is affected by an agent. The sentence-generation method is performed in two main processes: (i) resolve anaphoric pronoun and represent the semantics of the source pair of sentences; (ii) determine the ordinal relationship of processes and generate new reduced Vietnamese sentence. To evaluate the quality of summarization, we compare our generated sentences with sentence fusions which generated using K. Filippova [31]’s method as well as an enhancement by F. Boudin and E. Morin [16]. Using ROUGE measures [6 - 9], the results show that our method’s summaries are more precise and natural in overall.

Multilingual Automatic Translation Based on UNL: A Case Study for the Vietnamese Language

In the field of natural language processing, Universal Networking Language (UNL) has been used by various researchers as an inter-lingual approach to automatic machine translation. The UNL system consists of two main components, namely, EnConverter for converting text from a source language to UNL, and DeConverter for converting from UNL to a target language. Currently, many projects are researching how to apply UNL to different languages. In this paper, we introduce the tools that are UNL’s applications and discuss how to reuse them to encode a Vietnamese sentence into UNL expressions and decode UNL expressions into a Vietnamese sentence. The testing was done with about 1,000 Vietnamese sentences (a dictionary that includes 4573 entries and 3161 rules). In addition, we compare the proportion of sentences translated based on a direct method (Google Translator) and another one based on UNL.