The knowledge related with lexical items can be realized as including relations of meaning a cross words. Words that share a similarity of meaning are called to be synonymous, and words that share a contrary of meaning are called to be antonymous. Both of them are universal linguistic phenomenon that exist in terms of linguistic system of every language. The present study aims at finding out areas of difficulty that Iraqi EFL learners encounter in the use of synonymy and antonymy, both on the recognition and production levels. Also tries to detect the main reasons behind such difficulties. A diagnostic test of two parts, namely, recognition and production, is designed. The test is built to include two linguistic phenomenon which are: synonymy and antonymy. A random sample of one (100) third year College students of two Colleges of Education, in University of Baghdad and University of Diyala, (50) students each. Data analyzed were based on Cruse’s taxonomy (1986). The study has come up with the following conclusions: in spite of being students at an advanced level in learning English, they used a general lexical item, instead of their other synonyms and antonyms which imply a narrower sense of meaning. And although Iraqi EFL learners learn a number of synonym words and antonym words during their academic years of studying English, still they cannot utilize them correctly in context. Keywords: antonyms, production, recognition, synonyms
This research was aimed to determine the petrophysical properties (porosity, permeability and fluid saturation) of a reservoir. Petrophysical properties of the Shuiaba Formation at Y field are determined from the interpretation of open hole log data of six wells. Depending on these properties, it is possible to divide the Shuiaba Formation which has thickness of a proximately 180-195m, into three lithological units: A is upper unit (thickness about 8 to 15 m) involving of moderately dolomitized limestones; B is a middle unit (thickness about 52 to 56 m) which is composed of dolomitic limestone, and C is lower unit ( >110 m thick) which consists of shale-rich and dolomitic limestones. The results showed that the average formation water
... Show MoreAbstract :In this study, amygdaline in Iraqi plant seeds was extracted and isolated from their seeds matrix using reflux procedure and subsequently identified and determined by high performance liquid chromatography (HPLC) on reversed phase column of LC-18 (150mm x 4.6mm, 5?m )with actonitrile :water ( 50 : 50 ) as mobile phase at flow rate of ( 0.5 mL/min ) and detection at wavelength of 215 nm.The experimental results indicated that the linearity of calibration is in the range of 1.0-30.0 mg L-1amygdaline with the correlation coefficient of 0.9949. The limit of detection (LOD) and limit of quantitation (LOQ) for amygdaline were of 0.88 and 2.93 mg L-1 in standard pure sample. The mean recovery percent is 97.34±0.58 at 95% confidence inte
... Show MoreBackground: Ejection fraction have been used frequently
for assessment of the left ventricular function, but can be
associated with errors in which myocardial performance
index have been used as another parameter to measure the
left ventricular function.
Objective: selecting another echocardiography parameter
for the assessment of myocardial in function instead of the
ejection fraction.
Methods: 160 patients referred to the echocardiogram unit
from the period december 2007 to august 2008 requesting
assessment of left ventricular function. After clinical
examination, routine blood tests; chest x-ray and
electrocardiographic recording have been completed. All
patients informed to come for this unit af
In aspect-based sentiment analysis ABSA, implicit aspects extraction is a fine-grained task aim for extracting the hidden aspect in the in-context meaning of the online reviews. Previous methods have shown that handcrafted rules interpolated in neural network architecture are a promising method for this task. In this work, we reduced the needs for the crafted rules that wastefully must be articulated for the new training domains or text data, instead proposing a new architecture relied on the multi-label neural learning. The key idea is to attain the semantic regularities of the explicit and implicit aspects using vectors of word embeddings and interpolate that as a front layer in the Bidirectional Long Short-Term Memory Bi-LSTM. First, we
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