Semantic Analysis Towards English Substantive

The analysis describes semantic theories in defining English substantives “Someone/ Person/ People” with its references in Balinese kinship terms. The purpose is to explain several basic concepts of semantic theories in describing the meaning of specific terms through the analysis of their semantic features. Semantic features of Balinese kinship terms are explored by means of semantic evidence. The result of the analysis showed that semantic theories, Natural Semantics Metalanguage and Componential Analysis could simplify the complex meaning of Balinese kinships terms which were related semantically in order to understand their similarities and differences. Key words: Semantic features, English substantive (Someone/ Person/ People), Balinese Kinship term

Implementasi Explicit Semantic Analysis Berbahasa Indonesia Menggunakan Corpus Wikipedia Indonesia

Pengembangan terhadap Ujian Online Bahasa Indonesia dalam bentuk esai masih terus dilakukan sampai sekarang guna memperoleh nilai akurasi yang lebih baik dalam memberikan suatu penilaian. Penilaian yang sudah ada saat ini masih menggunakan kemiripan kata pada teks kunci jawaban dan teks jawaban. Cara tersebut memiliki kelemahan mengingat kata dengan tulisan berbeda dapat memiliki makna yang sama. Masalah tersebut dapat diatasi menggunakan skema vektor konsep. Vektor konsep bekerja pada level makna dari sebuah kata. Skema vektor konsep ini dapat diimplementasikan salah satunya menggunakan metode Explicit Semantic Analysis (ESA). Metode ESA memerlukan sebuah korpus yang besar, penelitian ini akan menggunakan korpus dari Artikel Wikipedia Indonesia. Dengan menggunakan metode ESA proses penilaian akan dilakukan dengan membandingkan kemiripan makna dari teks kunci jawaban dengan teks jawaban. Pengujian dilakukan dengan membandingkan 400 teks jawaban soal esai online dengan kunci jawabannya. Dari hasil pengujian tersebut didapatkan kesimpulan bahwa nilai percentage error metode ESA adalah 65%, di mana angka tersebut merupakan probabilitas error yang terlalu tinggi. Pengujian lain yang dilakukan adalah dengan membandingkan nilai percentage error metode ESA dengan metode lain seperti Cosine Similarity, Euclidean Distance, dan Jaccard yang memberikan konklusi bahwa metode ESA tidaklah lebih akurat dari metode-metode lain tersebut

Creating an Intelligent System for Bankruptcy Detection: Semantic data Analysis Integrating Graph Database and Financial Ontology

In this paper, we propose a novel intelligent methodology to construct a Bankruptcy Prediction Computation Model, which is aimed to execute a company’s financial status analysis accurately. Based on the semantic data analysis and management, our methodology considers the Semantic Database System as the core of the system. It comprises three layers: an Ontology of Bankruptcy Prediction, Semantic Search Engine, and a Semantic Analysis Graph Database

A Discourse Semantic Analysis of the Expository Paragraphs Written by the Eleventh Grade Students of SMAN 1 Kubu in the Academic Year 2013/2014 Based on Systemic Functional Linguistic Theory

This study aimed at describing and explaining the experiential meaning, interpersonal meaning and textual meaning as well as the nature of schematic structure in the students' writings under study. This study used descriptive qualitative approach with content analysis as its technique. In collecting the data, some techniques were used such as content analysis, document analysis and also interviewing. This study involved 24 students of the eleventh grade of SMAN 1 Kubu in the academic year 2013/2014. Thus, the object of this study was 24 students' expository writings analyzed through Systemic Functional Linguistics. From the result of the study, it was found out that the students expressed the experiential meaning by using six process types. Mostly, they used material process in their writings. The students expressed the interpersonal meaning of the text through the use of declarative, imperative, modality and personal pronoun. It was revealed that the expository texts under study were mostly constructed in declarative forms. The students expressed the textual meaning of their expository texts through developing themes and rhemes of the clauses constructing the texts. For that reason, the thematic development or progression was analyzed to recognize the textual meanings. Meanwhile, it was identified that twenty three expository writings were built in three main stages, while there is one text which was constructed in two stages or generic structures. The three stages cover introduction (thesis), main body (arguments) and conclusion (reiteration)

Semantic Network Analysis of Ontologies

A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures currently receive high attention in the Semantic Web community, there are only very few SNA applications, and virtually none for analyzing the structure of ontologies. We illustrate the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size

Thematically Reinforced Explicit Semantic Analysis

We present an extended, thematically reinforced version of Gabrilovich and Markovitch's Explicit Semantic Analysis (ESA), where we obtain thematic information through the category structure of Wikipedia. For this we first define a notion of categorical tfidf which measures the relevance of terms in categories. Using this measure as a weight we calculate a maximal spanning tree of the Wikipedia corpus considered as a directed graph of pages and categories. This tree provides us with a unique path of "most related categories" between each page and the top of the hierarchy. We reinforce tfidf of words in a page by aggregating it with categorical tfidfs of the nodes of these paths, and define a thematically reinforced ESA semantic relatedness measure which is more robust than standard ESA and less sensitive to noise caused by out-of-context words. We apply our method to the French Wikipedia corpus, evaluate it through a text classification on a 37.5 MB corpus of 20 French newsgroups and obtain a precision increase of 9-10% compared with standard ESA.Comment: 13 pages, 2 figures, presented at CICLing 201

An Analysis on Syntactic and Semantic Factors Found in Newspaper Headlines

As a type of media text, newspaper has an important role in human\u27s life because it presents various local, national and International information and events. In order to attract readers\u27 attention, journalists make the headlines as ambiguous and confusing as possible so that readers are curious to know the content of the whole story and they would read it. Moreover, in presenting the information or events, different reporters will have different linguistic choices which include the choice of words and expressions and different linguistic structures. Thus, this paper analyzes how the different linguistic choices and structures used in the headlines of The Jakarta Post and Indonesian Daily News would construct different linguistic representations of events in the world