Sentences In Order Research Articles

Word Order Variations in Shabaki

Word order types constitute the most well- established and frequently cited generalization in language typology. Basic or canonical word order tends to fall into two main types: SOV (about 48% of world languages) or SVO (about 41%). SOV is assumed to have been the predominant and unmarked word order in most of the oldest attested Indo-European languages, as well as in Iranian languages. This paper investigates the basic word order in Shabaki, a modern northwest language of the Indo-Iranian family spoken by the Shabaki minority in Iraq. This study also measures the word order variation and provides a typological description of this language. An examination of Shabaki data reveals that it follows SOV, OSV, SVO, VSO, OVS, and VOS word order patterns in mono-transitive sentences. The most frequent (predominant and unmarked) word order in declarative sentences in Shabaki is SOV where the initial position is occupied by a nominative noun phrase, but constituents can appear at any position, creating grammatical sentences with different discursive distributions. In ditransitive sentences, (S) DO V IO is proven to be three fold higher in number than (S) IO V DO. (S) DO IO V and (S) IO DO V were also found in data. Keywords: Agreement, basic word order, case, clitics, Indo-Iranian languages, language typology, Shabaki Introduction Grammatical relations in human languages, such as those between a noun phrase and the verb, are primarily expressed by means of three different morphosyntactic strategies: word order, case marking, and agreement (Croft 1990: 101). All languages, rather than relying on just one of these mechanisms, use some combination of the three. In this paper, it is the intention to explore what elements of these three strategies Shabaki language employs to indicate the relationship that a noun bears to the verb in a clause. It also tries to explore the order of objects in ditransitive sentences.

Sentence Selection using Latent Semantic Analysis for Automatic Question Generation in E-Learning System

The Current scenario of the educational system is highly utilizing computer-based technology. For the Teaching-Learning process, both the learners and teachers are highly preferred the online system i.e, E-Learning because of its user-friendly approach such as learning at anytime and anywhere. In the Online educational system, the E-Content plays a major role so the critical importance has to be provided in generating the E-Content. Currently, a large number of study materials are dumped into the internet which has reached the highest limit. The enormous amount of content with high volume leads the learner to skim or frustration in learning. Learners have to spend too much of time to understand their concept from the selected web page. The Tutor also faces the challenges in setting the question paper from this high volume of learning content. We have proposed the computer-assisted system to summarize the learning content of the material using Machine Learning techniques. The Latent Semantic Analysis reduces the size of the content without changing their originality. Finally, the singular value decomposition is used to select the important sentences in order to generate the Multiple Choice Questions (MCQ) to assess the knowledge level of the learner.

Hierarchical Attention Networks for Sentence Ordering

Modeling discourse coherence is an important problem in natural language generation and understanding. Sentence ordering, the goal of which is to organize a set of sentences into a coherent text, is a commonly used task to learn and evaluate the model. In this paper, we propose a novel hierarchical attention network that captures word clues and dependencies between sentences to address this problem. Our model outperforms prior methods and achieves state-of-the-art performance on several datasets in different domains. Furthermore, our experiments demonstrate that the model performs very well even though adding noisy sentences into the set, which shows the robustness and effectiveness of the model. Visualization analysis and case study show that our model captures the structure and pattern of coherent texts not only by simple word clues but also by consecution in context.

The Influce of Syntax on Learning The Second :The Example of Kurdish and Arabic

The research entitled (Effect of syntax level in learning the second language - Kurdish and Arabic language model). The aim of the research is to determine the effect of syntactic structures especially phrase structure in the process of learning the second language. The research is divided into three sections. The first section presents a theoretical framework on linguistic concepts and theories in the process of learning the second language. In the second part of the research we identified the syntactic structures and phrases in the Kurdish language. Among the phrases: noun phrase, verb phrase, adverb phrase and adjective phrase. We also defined concepts; such as: head, complement and adjunct in the structure of the internal structure of the phrases. We introduced syntax, sentence structure, and phrases order in sentence. The third part of the research is devoted to the syntactic structures and phrases in the Arabic language. We introduced the structure and structure of the sentence and the order of phrases syntax sentence. We discussed the concept of (Ordering) in the Arabic sentence. We also identified the similarities and differences between the structure of the phrases in the two languages (Kurdish and Arabic), and the most important research findings that knowledge of similarities and Syntactic differences between the two languages ​​is a factor in the learning of the second language.

비정물주어 속성서술문의 구조적·의미적 특징

In general, the event predication is expressed by verb sentence and the attribute predication is expressed by nominal and adjectival sentences. However, each type of sentence does not have a single meaning usuage, each of verbal, adjectival and nominal sentences can convey both event and attribute predications. This study explores special features of the attribute predication by taking a closer look at the adjectival and nominal attribute sentences that have the inanimate subject, with some additional investigation on the verbal sentence in order to supplement Choi (2016) that examined the verbal attribute sentence earlier. Regarding the data, this study drew the data from novels, essays, movie scenarios, textbooks, weelky magazines, dictionaries and the Mainichi Newspapers. I examined special features of the attribute predication in terms of the syntactic and semantic areas. With regrad to the structural features, I analyzed the characterization of sentence patterns, subjectization of non-nominative nouns, usuage of negative forms, characteristics of the compound sentence structure, and formulation of predication. With regard to the semantic features, I analyzed the type of meaning that can be conveyed by the verbal, adjectival and nominal sentences, and clarified the similarities and differences between their meaning types.

Discursive Creation Technique of English to Indonesian Subtitle in Harry Potter: The Chamber of Secrets Movie

The purpose of this paper is to analyze the used of discursive creation technique of English to Indonesian subtitle in Harry Potter: The Chamber of Secrets movie. This research is descriptive qualitative method. Data of this research are utterances or sentences in subtitle movie of bilingual edition, both English and Indonesian. While, the source of data of this research is Harry Potter: The Chamber of Secrets movie. The researchers only used one data source, it was document. In analyzing the data, the researchers used comparison method to compare between the SL and TL that were analyzed. The result of this research shows that discursive analysis divided into three catogories of language unit, they are word 23%, phrase 20%, and sentence 57%. Therefore, it can be concluded that the translator has many errors in the sentence translated because he choose the easy sentence in order to make viewer understand the subtitle.
 

A Study on Translation Strategies used in Visayan Folktales

This research paper focused on observing and analyzing the translation strategies utilized in translating Visayan folktales into English. The researcher aimed to analyze whether the translation strategies utilized in the translations of the selected Visayan folklores were still able to deliver the ideas found in the important lines as well as the message of the story. The purpose of this study was to gain further understanding as to which translation strategies were more evident in literary works. 3 (three) folktales rendered in Bisaya and were translated in English served as the research data for this study. The researcher read and compared the Visayan versions with their corresponding English translations in word, sentence, and textual level in order to identify the translation strategies which were employed. The results showed that the use of the oblique translation method was more evident. Among the seven sub-categories of an oblique translation, the translated versions exhibited the frequent use of borrowing, transposition, and adaptation. The evident use of an oblique translation was reflected by the type of text that was translated. The translation strategies used in the English versions were still able to relay the similar message that the Visayan version of the folktales contained.

Hierarchical sequence labeling for extracting BEL statements from biomedical literature

BackgroundExtracting relations between bio-entities from biomedical literature is often a challenging task and also an essential step towards biomedical knowledge expansion. The BioCreative community has organized a shared task to evaluate the robustness of the causal relationship extraction algorithms in Biological Expression Language (BEL) from biomedical literature.MethodWe first map the sentence-level BEL statements in the BC-V training corpus to the corresponding text segments, thus generating hierarchically tagged training instances. A hierarchical sequence labeling model was afterwards induced from these training instances and applied to the test sentences in order to construct the BEL statements.ResultsThe experimental results on extracting BEL statements from BioCreative V Track 4 test corpus show that our method achieves promising performance with an overall F-measure of 31.6%. Furthermore, it has the potential to be enhanced by adopting more advanced machine learning approaches.ConclusionWe propose a framework for hierarchical relation extraction using hierarchical sequence labeling on the instance-level training corpus derived from the original sentence-level corpus via word alignment. Its main advantage is that we can make full use of the original training corpus to induce the sequence labelers and then apply them to the test corpus.

A Discourse Analysis: Cohesion of the Introduction Section of Research Article

<em>As an effort to give contribution to the existing knowledge, it is expected for the undergraduate students to compose an engaging research article in order to convince the readers about the importance of the research article. However, there is only a little attention given to the articles written by the undergraduate students although it is considered very critical to examine whether the exposure of English academic writing has significantly enhances the writing competence of the students. Furthermore, as it is also very crucial to build a meaningful semantic meaning among the sentences in order to disclose the worthiness of the research article, it is essential to analyze the cohesion of the research article written by the undergraduate students. Henceforth, the present research is projected to investigate the cohesion of the research articles written by the undergraduate students of English Language Teaching. As the introduction section of research article is likely to be an area to portray the logical explanation of the research, the present research solely focuses on examining the cohesion of the introduction section of research article. By adopting a qualitative design and involving several steps to analyze the introduction section, it is revealed that the grammatical cohesion is considered to be the most utilized type of cohesion in writing the introduction section. Still, the lexical cohesion is also necessary to build an eloquent semantic meaning about the topic as well the importance of the research article.</em>

한국어 교재의 ‘-이/히/리/기-’ 피동 단원 분석과 교수 방안 제시

This paper aims to examine the presentation aspects of ‘-i/hi/li/gi-’ passive unit appearing in Korean textbooks, define problems, and create barriers to complement them. As a result of examining the textbooks of 15 universities, three problems were found in the presentation of the passive unit. First, the learning objective is to memorize the passive verbs themselves in which each affix is combined, rather than create proper passive sentences appropriately using passive verbs. Second, the list of passive verbs in textbooks contains mixed passive verbs that do not fulfill the original features of passive verbs. Further, only passive verbs without sample sentences are presented. Third, there are many difficulties in teaching and learning situations by presenting various passive sentences without identifying various passive sentences with differences in their features. To solve these problems, it was emphasized that the core of the passive unit is to learn passive verbs and further create passive sentences by taking the educational contents of the passive unit to a sentence level rather than a word level. Also, the paper asserted that considering the features and differences in passive sentences, the passive sentences be divided and presented by type and teach students the rules and constraints for creating passive sentences in order. (Soongsil University)

Quantum Distributions and Text Studies: Temperature and Literature

Rank-frequency distributions of words in texts have a number of similarities to particle distributions in statistical physics. This makes it possible to propose a new set of parameters by which texts that are examples of complex systems can be attributed. In particular, it is possible to show the relation of these parameters to the typological classification of languages by their level of analyticity and to illustrate the evolution within several language “lineages”. The proposed parameters are calculated from the frequency data of words rarely occurring in texts. It turns out that this part of а rank-frequency distribution is characterized by more stable behaviour, in contrast to the high- frequency lexicon certain other authors used in similar studies. One of the parameters used in classification is an analogue of temperature in physics. Its smaller values correspond to languages with a higher level of analyticity (less developed word inflection, replaced by a greater number of auxiliary words and a fixed word order in sentences). The proportion of rarely used vocabulary in such languages is different from languages in which inflection is well developed. Our approach is demonstrated using the example of translations of Antoine de Saint-Exupéry’s novella The Little Prince into nearly forty languages and various translations of the Gospel of John. The latter are used to study the historical development of language given that this religious text was translated in several different centuries. The results indicate new dimensions of previously known concepts. They are considered in the broader context of the linguo- statistical and linguo-philosophical achievements of Wilhelm von Humboldt, Morris Swadesh, Joseph Greenberg, Gabriel Altmann, Reinhard Köhler, and other linguists.

Discourse Coherence as a Cue to Reference in Word Learning: Evidence for Discourse Bootstrapping.

Word learning depends critically on the use of linguistic context to constrain the likely meanings of words. However, the mechanisms by which children infer word meaning from linguistic context are still poorly understood. In this study, we asked whether adults (n=58) and 2- to 6-year-old children (n=180) use discourse coherence relations (i.e., the meaningful relationships between elements within a discourse) to constrain their interpretation of novel words. Specifically, we showed participants videos of novel animals exchanging objects. These videos were accompanied by a linguistic description of the events in which we manipulated a single word within a sentence (and vs. because) in order to alter the causal and temporal relations between the events in the discourse (e.g., "One animal handed the baby to the other animal [and/because] the baby started crying in the talfa's arms"). We then asked participants which animal (the giver or the receiver) was the referent of the novel word. Across two experiments, we found evidence that young children used the causal and temporal relations in each discourse to constrain their interpretations of novel words.

Natural Language Generation Using Dependency Tree Decoding for Spoken Dialog Systems

In this paper, we propose a new natural language generation (NLG) method for spoken dialog systems and demonstrate its capacity. Studies on NLG often employ sequence decoding, which generates the words comprising a sentence in sequential order and uses the input generated by each word in the previous step. In contrast, we propose a decoding method that employs a sequence generated by traversing a dependency tree with feed input to a pair consisting of a parent and sibling in the dependency tree. As a result, the most important words are generated first, thereby enabling words with greater relevance to be fed into the process. At prediction time, our model generates dependency trees and converts the trees into sentences. The proposed decoding method was evaluated by re-implementing a semantically controlled long short-term memory structure for NLG, and the input and predicted sequence were converted to allow dependency tree decoding. The experimental results indicated that our suggested approach, i.e., dependency tree decoding, dramatically elevates the BLEU-score and naturalness. Furthermore, when creating sentences with ${n}$ -best using dependency tree decoding, the word diversity of the output sentences was increased by approximately 6%, offering a more diverse sentence pattern.

Does Semantic Constraint Modulate Combinatorial Processing? an fMRI study in Farsi

Does Semantic Constraint Modulate Combinatorial Processing? an fMRI study in Farsi

الترادف عند اللغويين و الأصوليين

Synonyms are words that have the same meaning in other words the same language. The language and the ushul fiqh have a view memorize the phenomenon of synonyms. The reality is that synonyms are found in Arabic, Al-Qur 'an and Al-hadith. Some experts view that it can be a word in the order of sentences replaced by synonyms, except da! am case of Al-Qur'an. Hi it's because the Qur'an, pronunciation and its meaning are Allah's revelations, read it worship.

Teach Your Robot Your Language! Trainable Neural Parser for Modeling Human Sentence Processing: Examples for 15 Languages

We present a recurrent neural network (RNN) that performs thematic role assignment and can be used for human–robot interaction (HRI). The RNN is trained to map sentence structures to meanings (e.g., predicates). Previously, we have shown that the model is able to generalize on English and French corpora. In this article, we investigate its ability to adapt to various languages originating from Asia or Europe. We show that it can successfully learn to parse sentences related to home scenarios in 15 languages, namely English, German, French, Spanish, Catalan, Basque, Portuguese, Italian, Bulgarian, Turkish, Persian, Hindi, Marathi, Malay, and Mandarin Chinese. Moreover, in the corpora, we have deliberately included variable complex sentences in order to explore the flexibility of the predicate-like output representations. This demonstrates that: 1) the learning principle of our model is not limited to a particular language (or particular sentence structures), but more generic in nature and 2) it can deal with various kind of representations (not only predicates), which enables users to adapt it to their own needs. As the model is inspired from neuroscience and language acquisition theories, this generic and language-independent aspect makes it a good candidate for modeling human sentence processing. Finally, we discuss the potential implementation of the model in a grounded robotic architecture.

Fast and scalable neural embedding models for biomedical sentence classification

BackgroundBiomedical literature is expanding rapidly, and tools that help locate information of interest are needed. To this end, a multitude of different approaches for classifying sentences in biomedical publications according to their coarse semantic and rhetoric categories (e.g., Background, Methods, Results, Conclusions) have been devised, with recent state-of-the-art results reported for a complex deep learning model. Recent evidence showed that shallow and wide neural models such as fastText can provide results that are competitive or superior to complex deep learning models while requiring drastically lower training times and having better scalability. We analyze the efficacy of the fastText model in the classification of biomedical sentences in the PubMed 200k RCT benchmark, and introduce a simple pre-processing step that enables the application of fastText on sentence sequences. Furthermore, we explore the utility of two unsupervised pre-training approaches in scenarios where labeled training data are limited.ResultsOur fastText-based methodology yields a state-of-the-art F1 score of.917 on the PubMed 200k benchmark when sentence ordering is taken into account, with a training time of only 73 s on standard hardware. Applying fastText on single sentences, without taking sentence ordering into account, yielded an F1 score of.852 (training time 13 s). Unsupervised pre-training of N-gram vectors greatly improved the results for small training set sizes, with an increase of F1 score of.21 to.74 when trained on only 1000 randomly picked sentences without taking sentence ordering into account.ConclusionsBecause of it’s ease of use and performance, fastText should be among the first choices of tools when tackling biomedical text classification problems with large corpora. Unsupervised pre-training of N-gram vectors on domain-specific corpora also makes it possible to apply fastText when labeled training data are limited.

Naar een betere dagvaarding

Abstract Improving the court summons. Effects of stylistic and structural revisions on summons comprehensions for readers of different literacy levels The traditional Dutch court summons does not help readers to understand the information it’s lawfully supposed to communicate. Especially lower literate and lower educated readers cannot deal with this document properly. In this study we compare reading success in four versions of a court summons. The content in all four is the same. The first, classic summons is normally being used throughout the legal professions involved in serving writs. In the second version the structure is manipulated by inserting headings and an altered order of both sentences and topics. In the third version only words and sentences have been manipulated, the structure however remained identical as in the classic summons. In the fourth version both manipulations are combined. The results indicate that when both style and structure are revised, readers with lower literacy and a lower educational level are no longer disadvantaged and show equal reading success. A reversed Matthew effect occurs in this revision. Therefore the authors suggest the new court summons should be revised on both structure and style.

Lexical and Syntactic Competency through Language Camp Activities Based on Multiple Intelligence Approach

The attempts of the present research were 1) to investigate the students’ problems in terms of lexical and syntactic usage in Language camp prior to the implementation of the activities based on multiple intelligence approach 2) to develop the students in terms of lexical and syntactic used in Language camp and 3) to compare the students’ learning achievement in terms of lexical and syntactic used in Language camp before and after implementing the activities based on multiple intelligence approach. The research sample, obtained by a purposive sampling technique, comprised 52 third-year students enrolled in Language Camp for English Skill Development course in the 2<sup>nd</sup> semester, Academic Year 2017. The research tools used in the study comprised of pre-and post-test, activities based on Multiple Intelligence approach, and standardized interview. The obtained quantitative data were computed by means, standard deviation, percentage, and independent sample t-test whereas the content analysis was done to elicit the qualitative data. The findings indicated that the students' problems of lexical and syntactic usage were categorized into two main issues: the confusion of using vocabularies and sentences and incorrect sentence orders. The results from the comparison before and after implementing the activities based on multiple intelligence approach revealed statistically significant differences at the .01 level. The students’ achievements were considerably improved after learning through the activities based on multiple intelligence approach and could employ each lexical and syntactic category with more confidence when retaking the exam.

A Critical Review of Grammar Teaching Methodologies in the Saudi Context

‘Grammar is the business of taking a language to pieces, to see how it works’ (Crystal, 1996, p. 6). The study of grammar has fascinated people for many years, especially in the field of second language acquisition (SLA). However, in recent years people became uncertain about its value. Consequently, some educational institutions ceased to teach it, others teach it very selectively (Crystal, 1996; Ellis, 2002). To know grammar means to know more about how to manipulate the parts of a sentence in order to provide a meaningful expression. Teaching grammar has been subjected to a tremendous change, particularly throughout the twentieth century. There has always been a development in thinking about the nature of language which has enabled people to see the point of the study and teaching of grammar. Also, there have been quite a number of adaptations of various methodologies of teaching grammar. This language component (grammar) has been always the centre of pedagogical attention. The aim of this project is to discuss the changing role of teaching grammar from a Saudi teacher’s perspective, and to explore why some Saudi EFL teachers might wish to change their approach to teaching grammar and how they might do so. In addition, the following section will try to shed light on some of the salient grammar methods throughout the field of English language teaching (ELT) and provide some implications for EFL teachers and learners.