A review on corpus annotation for arabic sentiment analysis

Mining publicly available data for meaning and value is an important research direction within social media analysis. To automatically analyze collected textual data, a manual effort is needed for a successful machine learning algorithm to effectively classify text. This pertains to annotating the text adding labels to each data entry. Arabic is one of the languages that are growing rapidly in the research of sentiment analysis, despite limited resources and scares annotated corpora. In this paper, we review the annotation process carried out by those papers. A total of 27 papers were reviewed between the years of 2010 and 2016

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

An Empirical Evaluation of Arabic-Specific Embeddings for Sentiment Analysis

International audienc

Liver fibrosis: consensus recommendations of the Asian Pacific Association for the Study of the Liver (APASL)

Liver fibrosis is a common pathway leading to cirrhosis, which is the final result of injury to the liver. Accurate assessment of the degree of fibrosis is important clinically, especially when treatments aimed at reversing fibrosis are being evolved. Liver biopsy has been considered to be the “gold standard” to assess fibrosis. However, liver biopsy being invasive and, in many instances, not favored by patients or physicians, alternative approaches to assess liver fibrosis have assumed great importance. Moreover, therapies aimed at reversing the liver fibrosis have also been tried lately with variable results. Till now, there has been no consensus on various clinical, pathological, and radiological aspects of liver fibrosis. The Asian Pacific Association for the Study of the Liver set up a working party on liver fibrosis in 2007, with a mandate to develop consensus guidelines on various aspects of liver fibrosis relevant to disease patterns and clinical practice in the Asia-Pacific region. The process for the development of these consensus guidelines involved the following: review of all available published literature by a core group of experts; proposal of consensus statements by the experts; discussion of the contentious issues; and unanimous approval of the consensus statements after discussion. The Oxford System of evidence-based approach was adopted for developing the consensus statements using the level of evidence from 1 (highest) to 5 (lowest) and grade of recommendation from A (strongest) to D (weakest). The consensus statements are presented in this review