Written communication is of utmost importance to the progress of scientific research. The speed of such development, however, may be affected by the scarcity of reviewers to referee the quality of research articles. In this context, automatic approac
hes that are able to query linguistic segments in written contributions by detecting the presence or absence of common rhetorical patterns have become a necessity. This paper aims to compare supervised machine learning techniques tested to accomplish genre analysis in Introduction sections of software engineering articles. A semi-supervised approach was carried out to augment the number of annotated sentences in SciSents (Avaliable on: ANONYMOUS). Two supervised approaches using SVM and logistic regression were undertaken to assess the F-score for genre analysis in the corpus. A technique based on logistic regression and BERT has been found to perform genre analysis highly satisfactorily with an average of 88.25 on F-score when retrieving patterns at an overall level.
This paper describes the approach that was developed for SemEval 2021 Task 7 (Hahackathon: Incorporating Demographic Factors into Shared Humor Tasks) by the DUTH Team. We used and compared a variety of preprocessing techniques, vectorization methods,
and numerous conventional machine learning algorithms, in order to construct classification and regression models for the given tasks. We used majority voting to combine the models' outputs with small Neural Networks (NN) for classification tasks and their mean for regression for improving our system's performance. While these methods proved weaker than modern, deep learning models, they are still relevant in research tasks because of their low requirements on computational power and faster training.
The reported work is a description of our participation in the Classification of COVID19 tweets containing symptoms'' shared task, organized by the Social Media Mining for Health Applications (SMM4H)'' workshop. The literature describes two machine l
earning approaches that were used to build a three class classification system, that categorizes tweets related to COVID19, into three classes, viz., self-reports, non-personal reports, and literature/news mentions. The steps for pre-processing tweets, feature extraction, and the development of the machine learning models, are described extensively in the documentation. Both the developed learning models, when evaluated by the organizers, garnered F1 scores of 0.93 and 0.92 respectively.
The last years have shown rapid developments in the field of multimodal machine learning, combining e.g., vision, text or speech. In this position paper we explain how the field uses outdated definitions of multimodality that prove unfit for the mach
ine learning era. We propose a new task-relative definition of (multi)modality in the context of multimodal machine learning that focuses on representations and information that are relevant for a given machine learning task. With our new definition of multimodality we aim to provide a missing foundation for multimodal research, an important component of language grounding and a crucial milestone towards NLU.
Within the last few years, the number of Arabic internet users and Arabic online content is in exponential growth. Dealing with Arabic datasets and the usage of non-explicit sentences to express an opinion are considered to be the major challenges in
the field of natural language processing. Hence, sarcasm and sentiment analysis has gained a major interest from the research community, especially in this language. Automatic sarcasm detection and sentiment analysis can be applied using three approaches, namely supervised, unsupervised and hybrid approach. In this paper, a model based on a supervised machine learning algorithm called Support Vector Machine (SVM) has been used for this process. The proposed model has been evaluated using ArSarcasm-v2 dataset. The performance of the proposed model has been compared with other models submitted to sentiment analysis and sarcasm detection shared task.
تعرض المحاضرة شرح عن علم البيانات وعلاقته بعلم الإحصاء والتعلم الآلي وحالتين دراسيتين عن دور عالم البيانات في تصميم حلول تعتمد على استخراج المعرفة من حجم كبير من البيانات المتوفرة, كما يتم عرض أهم المهام في المؤتمرات العلمية التي يمكن المشاركة بها لطلاب المعلوماتية المهتمين بهذا المجال
In this work, we compare three different modeling approaches for the scores of soccer matches with regard to their predictive performances based on all matches from the four previous FIFA World Cups 2002 – 2014: Poisson regression models, random
forests and ranking methods.
حظيت نمذجة وتوقع السلاسل الزمنية بأهمية كبيرة في العديد من المجالات التطبيقية كالتنبؤ بالطقس وأسعار العملات ومعدلات استهلاك الوقود والكهرباء، إن توقع السلاسل الزمنية من شأنه أن يزود المنظمات والشركات بالمعلومات الضرورية لاتخاذ القرارات الهامة، وبسبب
أهمية هذا المجال من الناحية التطبيقية فإن الكثير من الأعمال البحثية التي جرت ضمنه خلال السنوات الماضية، إضافةً إلى العدد الكبير من النماذج والخوارزميات التي تم اقتراحها في أدب البحث العلمي والتي كان هدفها تحسين كل من الدقة والكفاءة في نمذجة وتوقع السلاسل الزمنية.
يهدف التنقيب في النصوص بشكل عام إلى تحليل النصوص لاستخلاص معارف ذات جودة عالية من عدة مصادر نصية، والربط فيما بينها لتشكيل حقائق وفرضيات جديدة. تعد الأوراق البحثية التمثيل الأكثر اكتمالاً للمعرفة البشرية. وقد ساهمت حركة "الوصول المفتوح" إلى الأوراق ا
لبحثية، بالإضافة إلى ازدهار حقل التعلم الآلي في الآونة الأخيرة وتوفر الأدوات البرمجية والعتادية بكلف منخفضة نسبياً، بتداعي الحواجز المعيقة لعملية التنقيب في نصوص الأوراق البحثية.
في تتمة هذه الدراسة سنستعرض مجموعة من أساليب التنقيب في النصوص العلمية من حيث أهميتها، مجالات استخدامها، وطرق تطبيقها.
In recent years, time-critical processing or real-time processing and analytics of bid data have received a significant amount of attentions. There are many areas/domains where real-time processing of data and making timely decision can save thousand
s of human lives, minimizing the risks of human lives and resources, enhance the quality of human lives, enhance the chance of profitability, efficient resources management etc. This paper has presented such type of real-time big data analytic applications and a classification of those applications. In addition, it presents the time requirements of each type of these applications along with its significant benefits. Also, a general overview of big data to describe a background knowledge on this scope.
