The Translation of Reiteration

Lexical cohesion has been a serious issue as it is one of the important features in a text. Every writer must consider kinds of lexical devices in writing a text and so do the translator. The main problem in translating the lexical cohesion devices is the different structure between two languages. One device can be applied in one language but not in other language. This journal analyzed the translation of reiteration as part of lexical cohesion devices appeared in the short stories The Black Cat and The Cask of Amontillado written by American writer, Edgar Allan Poe. The short stories were translated by two Indonesian translators namely Anton Kurnia and Shinta Dewi. This research is conducted to share the practice of translating literature works especially a short story which contains a lot of lexical cohesion devices and to give contribution to the development of translation as part of linguistic studies. In doing the research, qualitative and quantitative method is applied including observation, interviews, or document reviews. In the source text, it was found 120 of lexical cohesion devices in the short story ”The Cask of Amontillado” and 187 lexical cohesion devices in the short story “The Black Cat”. The results obtained from this research were that Anton Kurnia translated 73% of lexical cohesion devices in the source language into the target language. Meanwhile, Shinta Dewi translated 94% of lexical cohesion devices in the source text to the target text

Combining Visual Layout and Lexical Cohesion Features for Text Segmentation

We propose integrating features from lexical cohesion with elements from layout recognition to build a composite framework. We use supervised machine learning on this composite feature set to derive discourse structure on the topic level. We demonstrate a system based on this principle and use both an intrinsic evaluation as well as the task of genre classification to assess its performance

A Novel Approach Towards Automatic Text Summarization Using Lexical Chains

Text summarization is a process of extracting text by virtue of reduction of document contents while preserving the salient information intact. By using different set of parameters like position, format and type of sentences in an input text, frequency of words in a text etc., techniques have been developed. But the parameters vary depending on source of input texts. This in turn affects the performance of the algorithms. In this paper, we present a new method of automatic text summarization by making use of lexical cohesion in the text. Until now lexical chains have been used to model lexical cohesion. These lexical chains are sequences of words having semantic relations between them. In our proposed algorithm, we have used a modification of lexical chains to model the relationships that exist between words. DOI: 10.17762/ijritcc2321-8169.15081

Using NLP to build the hypertextuel network of a back-of-the-book index

Relying on the idea that back-of-the-book indexes are traditional devices for navigation through large documents, we have developed a method to build a hypertextual network that helps the navigation in a document. Building such an hypertextual network requires selecting a list of descriptors, identifying the relevant text segments to associate with each descriptor and finally ranking the descriptors and reference segments by relevance order. We propose a specific document segmentation method and a relevance measure for information ranking. The algorithms are tested on 4 corpora (of different types and domains) without human intervention or any semantic knowledge

The Use of yig-cha and chos-kyi-rnam-grangs in Computing Lexical Cohesion for Tibetan Topic Boundary Detection

To properly implement a simple Tibetan Information Retrieval (IR) system segmentation of one form or another (n-gram, POS-tagging, dictionary substring matching, etc.) must be performed (see Hackett (2000b)). To take Tibetan indexing to a more sophisticated level however, some form of topic detection must be employed. This paper reports the results of a pilot study on the application to Tibetan of one technique for topic boundary detection: Lexical Cohesion. The resources developed and deployed, the theoretical model used, and its potential applications are discussed