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Damascus University ورقة بحثية
Publication date
2004
fields
Arabic
and research's language is
العربية
Created by
Shamra Editor
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أبو علي الحسن بن أحمد الفارسي من أفذاذ علم العربية الذين أسهموا في إِرساء بنيانه الشامخ، و استنباط علله، و إِجراء قياسه. و أهل العلم مجمعون على إِمامة أبي علي في هذا العلم الذي أمحضه نفسه، و أَخَلصه حياته.
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References used
الإِمتاع والمؤانسة لأبي حيان، تحقيق أمين والزين والإبياري، لجنة التأليف، القاهرة، 1953 م.
بغية الوعاة للسيوطي، تحقيق محمد أبو الفضل إِبراهيم، مطبعة عيسى البابي الحلبي، القاهرة، 1964 م.
بقية الخاطريات لابن جنِّي، تحقيق د. محمد أحمد الدالي، مطبوعات مجمع اللُّغة العربية بدمشق.
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Evidence association in criminal cases is dividing a set of judicial evidence into several non-overlapping subsets, improving the interpretability and legality of conviction. Observably, evidence divided into the same subset usually supports the same
claim. Therefore, we propose an argumentation-driven supervised learning method to calculate the distance between evidence pairs for the following evidence association step in this paper. Experimental results on a real-world dataset demonstrate the effectiveness of our method.
We rely on arguments in our daily lives to deliver our opinions and base them on evidence, making them more convincing in turn. However, finding and formulating arguments can be challenging. In this work, we present the Arg-CTRL - a language model fo
r argument generation that can be controlled to generate sentence-level arguments for a given topic, stance, and aspect. We define argument aspect detection as a necessary method to allow this fine-granular control and crowdsource a dataset with 5,032 arguments annotated with aspects. Our evaluation shows that the Arg-CTRL is able to generate high-quality, aspect-specific arguments, applicable to automatic counter-argument generation. We publish the model weights and all datasets and code to train the Arg-CTRL.
In this paper, we focus on identifying interactive argument pairs from two posts with opposite stances to a certain topic. Considering opinions are exchanged from different perspectives of the discussing topic, we study the discrete representations f
or arguments to capture varying aspects in argumentation languages (e.g., the debate focus and the participant behavior). Moreover, we utilize hierarchical structure to model post-wise information incorporating contextual knowledge. Experimental results on the large-scale dataset collected from CMV show that our proposed framework can significantly outperform the competitive baselines. Further analyses reveal why our model yields superior performance and prove the usefulness of our learned representations.
To fully model human-like ability to ask questions, automatic question generation (QG) models must be able to produce multiple expressions of the same question with different levels of detail. Unfortunately, existing datasets available for learning Q
G do not include paraphrases or question variations affecting a model's ability to learn this capability. We present FIRS, a dataset containing human-generated fact-infused rewrites of questions from the widely-used SQuAD dataset to address this limitation. Questions in FIRS were obtained by combining a given question with facts of entities referenced in the question. We study a double encoder-decoder model, Fact-Infused Question Generator (FIQG), for learning to generate fact-infused questions from a given question. Experimental results show that FIQG effectively incorporates information from facts to add more detail to a given question. To the best of our knowledge, ours is the first study to present fact-infusion as a novel form of question paraphrasing.
Poor design is a major factor in reducing the overall performance of construction projects as well as being directly responsible for many projects cost overrun, delay and being plagued with rework,
variations and disputation.
This research discusse
s the main factors affecting Design Quality in the Construction Industry in Syria.
The result is based on a number of interviews and questionnaire surveys involving 54 designers, contractors and construction professionals. The survey identified 45 different factors classified in
categories related to designer, management of the design process, owner and design documents. The severity of those factors was measured by the level of importance and was ranked according to the
importance index.
It was concluded that, insufficient overall design time, method of selecting the designer based on the lowest price offer, lack of documentation and changes client’s requirements were the most important factors. The results show that designers and contractors have different perceptions of the quality of
design factors.
The aim of this study is to assist all construction parties to plan effectively before starting a project during the design phase in order to minimize problems and eliminate extra costs needed to complete
the defective design. Finally, some recommendations were made in order to improve the design quality in the construction industry.
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