Comparative Basic-Words of Standard Arabic Palestinian and Tunisian

Abstract

This paper studies comparative linguistics on the process of word-formation that occurs in Modern Standard Arabic (MSA), Palestinian Arabic (PLS), and Tunisian Arabic (TNS). It is addressed to portray the process of the verb, adjective, and noun formation in three Arabic languages by using Plag’s theory and to identify sameness and contrariness of basic words by using Hock’s theory. This study used 220 of Morris Swadesh's basic vocabulary as the main guidelines for obtaining data. The criteria were adopted to analyze the data were orthographic, sound-change, phonological and morpheme contrast. This research used descriptive qualitative. The source of the data was basic-word vocabulary. The data were gathered by conducting an in-depth interview with five post-graduate students of the Arabic Department at Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta as informants to get information. The data were analyzed by using structural linguistics, especially phonology, morphology, and semantics. This investigation informed several aspects of findings such as processes of cognates, back-formation, phonological variations, prefixes, clipping, derivation, acronym, loanword, blending, and metathesis of MSA, PLS and TNS. Using the Swadesh vocabulary list, the results of this study found 207 vocabularies for each language, such as MSA, Tunisian, and Palestinian. Using word categorization, it has found that these vocabularies have categorized into five words classes, namely, nouns, adjectives, verbs, adverbs, determiner.

Collocation Features in Translated Texts Based on English Analogy Corpus

“…For the recognition of relationship categories of unlabeled compound sentences, deep learning methods are mostly used for the recognition of relationship categories of unlabeled compound sentences due to the lack of relationship words and the absence of obvious manual recognition features [14]. Li et al [15] used an attention-based mechanism of convolutional neural network on a Chinese chapter book library [16] for the recognition of unlabeled compound sentence relations. Algburi and Igaab [17] combined word vectors with lexical features as the input of the model and used CNN to classify unlabeled complex sentence relations.…”