الأمل هو جانب أساسي من استقرار الصحة العقلية والانتعاش في كل فرد في هذا العالم سريع المتغير.ستكون أي أدوات وأساليب تم تطويرها للكشف والتحليل وتوليد خطاب الأمل مفيدا.في هذه الورقة، نقترح نموذجا على اكتشاف الأمل في الأمل للكشف تلقائيا عن محتوى الويب الذي قد يلعب دورا إيجابيا في نشر العداء على وسائل التواصل الاجتماعي.نحن نقوم بإجراء التجارب من خلال الاستفادة من نماذج معالجة ما قبل المعالجة والنقل.لاحظنا أن نموذج بيرت متعدد اللغات المدرب مسبقا مع الشبكات العصبية للتنزل أعطى أفضل النتائج.تصنيفنا في المرتبة الأولى والثالث والرابع في المرتبة الإنجليزية ومالايالامية والإنجليزية ومجموعات البيانات المختلطة من التاميل والإنجليزية.
Hope is an essential aspect of mental health stability and recovery in every individual in this fast-changing world. Any tools and methods developed for detection, analysis, and generation of hope speech will be beneficial. In this paper, we propose a model on hope-speech detection to automatically detect web content that may play a positive role in diffusing hostility on social media. We perform the experiments by taking advantage of pre-processing and transfer-learning models. We observed that the pre-trained multilingual-BERT model with convolution neural networks gave the best results. Our model ranked first, third, and fourth ranks on English, Malayalam-English, and Tamil-English code-mixed datasets.
References used
https://aclanthology.org/
In this paper, we describe our approach towards utilizing pre-trained models for the task of hope speech detection. We participated in Task 2: Hope Speech Detection for Equality, Diversity and Inclusion at LT-EDI-2021 @ EACL2021. The goal of this tas
Due to the development of modern computer technology and the increase in the number of online media users, we can see all kinds of posts and comments everywhere on the internet. Hope speech can not only inspire the creators but also make other viewer
In a world with serious challenges like climate change, religious and political conflicts, global pandemics, terrorism, and racial discrimination, an internet full of hate speech, abusive and offensive content is the last thing we desire for. In this
This paper aims to describe the approach we used to detect hope speech in the HopeEDI dataset. We experimented with two approaches. In the first approach, we used contextual embeddings to train classifiers using logistic regression, random forest, SV
Analysis and deciphering code-mixed data is imperative in academia and industry, in a multilingual country like India, in order to solve problems apropos Natural Language Processing. This paper proposes a bidirectional long short-term memory (BiLSTM)