Semantic Levels Of Analysis Research Articles

Descriptive Analysis of Passive Voice Sentence Through Levels Of Semantic Analysis in the Story of A Study In Scarlet By Sir Arthur Conan Doyle

Learning English as Foreign Language is not that easy in Indonesia. Mostly learners will face some difficulties in learning it as people speak English as foreign language, and the opportunity to interact with English native speakers is very little. The function of English native teachers is to teach English naturally. Therefore, in this research, the researcher offers one of English grammar features to guide the EFL leaners understand English naturally. In this study, the researcher focusses on the level of semantic analysis which is actually a very basic lesson to learn English. The research methodology used is descriptive qualitative research. The data are passive voice constructions consisting of 42 passive voice sentences from a novel of Sherlock Holmes by the title “A Study in Scarlet”. The data are examined by the researcher herself through a software feature on online application. Afterward, the data are analyzed though levels of semantic analysis. There are three key themes which are analyzed: reference, sense relation and thematic role. In summary, the levels of semantic analysis is successful in analyzing the reference which naturally helps the learners memorize many new words also successful in both knowing the sense relation and thematic role which guide the learners think English word just like first language or second language learners.

O wyrazach złożonych z komponentem арт- w języku bułgarskich mediów

This paper discusses the problem of Bulgarian compound nouns with the component арт-. I would like to draw your attention to several significant features of the compound words in question. It mainly concerns the feature of brevity, the creation of new structures on the basis of a simple model based on analogy as well as the fact that the feature of formal transparency of compounds is accompanied by a large degree of semantic density. At the level of semantic analysis I used the text linguistic approach and emphasized the usefulness of cognitive methods of interpreting the meaning of the discussed type of words. I put an accent on the semantic blur of derivatives used in the language of the mass media out of context.

A Linguistic Stylistic Analysis of Henrik Ibsen’s <i>A Doll’s House</i>

Literary texts have largely been analysed from the perspective of literary criticism, literary stylistics. This paper is a linguistic stylistic analysis of Henrik Ibsen’s play, <i>A Doll’s House</i>. The principal aim of the paper of the paper is to identify and analyse linguistic features used in the play. For a systematic analysis, these linguistic features are clustered into phonological, graphological, lexical, grammatical and semantic levels of analysis. <i>A Doll’s House</i> is a play that explores family relationships, friendship and patriarchy among other thematic concerns. Helmer Torvald, one of the main characters, is the embodiment of hegemonic masculinity in the play. The study adopted a desktop research; the researcher read the text and identified linguistic features which he intuitively felt to be stylistically foregrounded. The results show that the author has effectively used linguistic features to develop the themes and characters of the play. The study concludes that stylistics provides the linguistic toolkit instrumental not only for the analysis and understanding of a given text, but also for the performance of the acted forms of art like a play (drama). Linguistic stylistics is a tool that can be used to analyse any form of text.

Semantic role induction in Persian: An unsupervised approach by using probabilistic models

Semantic roles describe the relation between a predicate (typically a verb) and its arguments. Semantic role labeling is a Natural Language Processing task that extracts these relations in the sentences. Different applications such as machine translation and question answering benefit from this level of semantic analysis. The creation of semantic role-annotated data is an obstacle to develop supervised learning systems, so we present a novel unsupervised approach to semantic role induction task. In our approach, which is formulized as a clustering method, the argument instances of the verb are clustered into semantic role classes specified for that verb. We present a Bayesian model for learning argument structure from un-annotated text and estimate the model parameters using expectation maximization method. Clustering of argument instances of a verb, which have semantic and syntactic similarities, can be a promising approach for unsupervised learning of their semantic roles. The only linguistic knowledge, which is prepared for linking the argument instances to semantic clusters is extracted from a verb valance lexicon. Our evaluation results on Persian language show that our system in both small and large training datasets works better than a strong baseline proposed by ([Lang and Lapata 2010][1]) which its idea is developed in Persian. We have used purity and inverse purity measures to assess the quality of the proposed semantic role clustering method. The results indicate the improvement about 9.73 and 1.65% in small dataset and 2.85 and 0.67% in large dataset in purity and inverse purity, respectively. [1]: #ref-20

Both sides get the point: Hemispheric sensitivities to sentential constraint

Behavioral studies have consistently reported striking differences in the impact of sentence-level information on the processing of words presented in the right (RVF) versus the left (LVF) visual field, with context effects apparent only for RVF items. The consistent lack of such effects in the LVF has been taken to mean that right hemisphere language comprehension is largely insensitive to message-level meaning. We used the functional specificity afforded by event-related potential measures to assess this claim. Target words completing strongly and weakly constraining sentence contexts, in which constraint arose at the sentence level rather than from lexical associations, were presented laterally in the LVF or RVF. Increased constraint significantly reduced N400 amplitudes with presentation in both VFs, with no differences in the timing or amplitude of these effects. These results are inconsistent with the view that the VF asymmetries found in behavioral measures reflect differential hemispheric capacities at the level of semantic analysis and integration, although VF-based differences on earlier components (P2) suggest asymmetries in the impact of sentential context on perceptual aspects of word processing in the two hemispheres.