Hyperpartisan news show an extreme manipulation of reality based on an underlying and extreme ideological orientation. Because of its harmful effects at reinforcing one's bias and the posterior behavior of people, hyperpartisan news detection has bec
ome an important task for computational linguists. In this paper, we evaluate two different approaches to detect hyperpartisan news. First, a text masking technique that allows us to compare style vs. topic-related features in a different perspective from previous work. Second, the transformer-based models BERT, XLM-RoBERTa, and M-BERT, known for their ability to capture semantic and syntactic patterns in the same representation. Our results corroborate previous research on this task in that topic-related features yield better results than style-based ones, although they also highlight the relevance of using higher-length n-grams. Furthermore, they show that transformer-based models are more effective than traditional methods, but this at the cost of greater computational complexity and lack of transparency. Based on our experiments, we conclude that the beginning of the news show relevant information for the transformers at distinguishing effectively between left-wing, mainstream, and right-wing orientations.
The combination of multilingual pre-trained representations and cross-lingual transfer learning is one of the most effective methods for building functional NLP systems for low-resource languages. However, for extremely low-resource languages without
large-scale monolingual corpora for pre-training or sufficient annotated data for fine-tuning, transfer learning remains an understudied and challenging task. Moreover, recent work shows that multilingual representations are surprisingly disjoint across languages, bringing additional challenges for transfer onto extremely low-resource languages. In this paper, we propose MetaXL, a meta-learning based framework that learns to transform representations judiciously from auxiliary languages to a target one and brings their representation spaces closer for effective transfer. Extensive experiments on real-world low-resource languages -- without access to large-scale monolingual corpora or large amounts of labeled data -- for tasks like cross-lingual sentiment analysis and named entity recognition show the effectiveness of our approach. Code for MetaXL is publicly available at github.com/microsoft/MetaXL.
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.
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
paper, we work to identify and promote positive and supportive content on these platforms. We work with several transformer-based models to classify social media comments as hope speech or not hope speech in English, Malayalam, and Tamil languages. This paper portrays our work for the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI 2021- EACL 2021. The codes for our best submission can be viewed.
Language as a significant part of communication should be inclusive of equality and diversity. The internet user's language has a huge influence on peer users all over the world. People express their views through language on virtual platforms like F
acebook, Twitter, YouTube etc. People admire the success of others, pray for their well-being, and encourage on their failure. Such inspirational comments are hope speech comments. At the same time, a group of users promotes discrimination based on gender, racial, sexual orientation, persons with disability, and other minorities. The current paper aims to identify hope speech comments which are very important to move on in life. Various machine learning and deep learning based models (such as support vector machine, logistics regression, convolutional neural network, recurrent neural network) are employed to identify the hope speech in the given YouTube comments. The YouTube comments are available in English, Tamil and Malayalam languages and are part of the task EACL-2021:Hope Speech Detection for Equality, Diversity and Inclusion''.
This paper presents the characterization of sea wave behavior in some areas of Lattakia
shore, through monitoring for more than one site different from each other by terrain,
water depth and bottom type (sand, rocks).
It also presents results of m
easurements of wave height values and their period at
breakwater area of Lattakia port, and also shows results of power calculations trans- mitted
with waves and speed of those waves, and brings a comparison of energy values calculated
using different experimental equations is being used globally, and shows that wave's speed
and period are independent from each other.
Also shows that it is possible to apply wave power techniques in Syria ,relying on principle
of high strength and low height , best way to achieve this is through hydraulic circuits ,and
installation of a central system and several subsystems connected to it ,this provides a
continuous flow of power.
Purpose: Lacunar infarcts is an important stroke subgroup with unique clinical and
pathologic features, but risk factors for lacunar infarcts have been rarely documented. To
address this matter, we studied 65 patients had lacunar infarction at Depa
rtment of
Neurology , Tishreen University Hospital , Lattakia , Syria , and 65 controls during the
period between May 2017 – May 2018 .
Methods: We obtained information concerning risk factor exposure status among the
patients by a structured questionnaire , we recorded age , sex , blood pressure , glucose ,
heart diseases , cigarette smoking , alcohol drinking , physical exercise for patiens and
controls .We do laboratory tests and ECG and echocardiography and CT brain or MRI .
Results: Significantly increasing the risk of lacunar stroke were hypertension
(P-value=0.0001) and (OR=9.9), Current smokers (P-value=0.002) and (OR=5.2), diabetes
(P-value=0.001) and (OR=5.3) , whereas frequent physical exercise was associated with a
significantly decreased risk) P-value=00001( and (OR=2.6). There was no risk of
lacunar stroke associated with heart disease (P-value=0.6) (OR=0.8) , high cholesterol (Pvalue=
1) and (OR=1) , alcohol drinking (P-value=0.7) and (OR=0.8).
Conclusions: Patients with hypertension or diabetes, current smokers, those who
have not heart disease, are at a higher risk of lacunar stroke, whereas those who undertake
regular physical exercise may be at lower risk.
The aim of this study is to collect more information about
Congenital hypothyroidism (CH) in Syria due to it being one of the
most common causes of preventable mental retardation if detected
at early stage and treatment is preventable, and to the
absence of any
published statistics regarding it in our country, and to emphasize on
the necessity of applying a mandatory newborn screening program
for early diagnosis of congenital hypothyroidism to improvement of
child’s life, by investigating the adequacy of the clinical
determination the physician's experience to get an early diagnosis.
This research was carried out in a field during the (2016)
season in Beit AL Raheb village . The agriculture was in (protected
and field under normal conditions) . two wheat variets were used :
Cham1 and Cham4.
This study was performed in order to determine the temperature
rise under human dentin discs of different thicknesses of primary teeth
during the light curing process with conventional halogen lamp and
LED.
