تصف هذه الورقة إدخال مجموعة الأبحاث سيناء في مهمة SMM4H الرئيسية على تحديد المهن والمهن في وسائل التواصل الاجتماعي ذات الصلة بالصحة.على وجه التحديد، شاركنا في المهمة 7A: Tweet تصنيف ثنائي لتحديد ما إذا كانت تغريدة تحتوي على تذوق من المهن أم لا، وكذلك في المهمة 7 ب: كشف إزاحة وتصنيف NER الذي يهدف إلى تذكر المهن وتصنيفها عن التمييز بين المهن وحضال العمل.
This paper describes the entry of the research group SINAI at SMM4H's ProfNER task on the identification of professions and occupations in social media related with health. Specifically we have participated in Task 7a: Tweet Binary Classification to determine whether a tweet contains mentions of occupations or not, as well as in Task 7b: NER Offset Detection and Classification aimed at predicting occupations mentions and classify them discriminating by professions and working statuses.
References used
https://aclanthology.org/
ProfNER-ST focuses on the recognition of professions and occupations from Twitter using Spanish data. Our participation is based on a combination of word-level embeddings, including pre-trained Spanish BERT, as well as cosine similarity computed over
This paper presents our contribution to the ProfNER shared task. Our work focused on evaluating different pre-trained word embedding representations suitable for the task. We further explored combinations of embeddings in order to improve the overall results.
Social media texts such as blog posts, comments, and tweets often contain offensive languages including racial hate speech comments, personal attacks, and sexual harassment. Detecting inappropriate use of language is, therefore, of utmost importance
In this paper we study pejorative language, an under-explored topic in computational linguistics. Unlike existing models of offensive language and hate speech, pejorative language manifests itself primarily at the lexical level, and describes a word
Given the current social distancing regulations across the world, social media has become the primary mode of communication for most people. This has isolated millions suffering from mental illnesses who are unable to receive assistance in person. Th