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Register a new userWord Embeddings, Cosine Similarity and Deep Learning for Identification of Professions \& Occupations in Health-related Social Media
Word Embeddings، التشابه الجيبكي والتعلم العميق لتحديد المهن \ والمهن في وسائل التواصل الاجتماعي المرتبط بالصحة
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يركز Profner-St على اعتراف المهن والمهن من تويتر باستخدام البيانات الإسبانية.تعتمد مشاركتنا على مزيج من Adgeddings على مستوى الكلمات، بما في ذلك بيرت الإسبانية المدربة مسبقا، بالإضافة إلى تشابه التموين المحسوبة فوق مجموعة فرعية من الكيانات التي تعمل كمدخل للحصول على بنية فك تشفير التشفير مع آلية الاهتمام.أخيرا، حقق أفضل درجة لدينا قياس F1 من 0.823 في مجموعة الاختبار الرسمية.
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 a subset of entities that serve as input for an encoder-decoder architecture with attention mechanism. Finally, our best score achieved an F1-measure of 0.823 in the official test set.
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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.
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.
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