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Register a new userView Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated Data
عرض التقطير مع بيانات غير مسفولة لاستخراج تأثيرات المخدرات الضارة من البيانات التي تم إنشاؤها من قبل المستخدم
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نقدم خوارزمية استنادا إلى محولات متعددة الطبقات لتحديد ردود الفعل الدوائية الضارة (ADR) في بيانات وسائل التواصل الاجتماعي.يعتمد نموذجنا على خصائص المشكلة وخصائص ASTDDings Word السياقي لاستخراج وجهات نظرتين من المستندات.ثم يتم تدريب المصنف على كل طريقة عرض لتسمية مجموعة من المستندات غير المستخدمة لاستخدامها كتهيئة لتصنيف جديد في الرأي الآخر.أخيرا، يتم تدريب المصنف التهيئي في كل طريقة عرض باستخدام أمثلة التدريب الأولي.قمنا بتقييم نموذجنا في أكبر مجموعة بيانات ADR المتاحة للجمهور.تشهد التجارب أن نموذجنا يتفوق بشكل كبير على النماذج القائمة على المحولات مسبقا على البيانات الخاصة بالمجال.
We present an algorithm based on multi-layer transformers for identifying Adverse Drug Reactions (ADR) in social media data. Our model relies on the properties of the problem and the characteristics of contextual word embeddings to extract two views from documents. Then a classifier is trained on each view to label a set of unlabeled documents to be used as an initializer for a new classifier in the other view. Finally, the initialized classifier in each view is further trained using the initial training examples. We evaluated our model in the largest publicly available ADR dataset. The experiments testify that our model significantly outperforms the transformer-based models pretrained on domain-specific data.
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User-generated texts include various types of stylistic properties, or noises. Such texts are not properly processed by existing morpheme analyzers or language models based on formal texts such as encyclopedias or news articles. In this paper, we pro
pose a simple morphologically tight-fitting tokenizer (K-MT) that can better process proper nouns, coinages, and internet slang among other types of noise in Korean user-generated texts. We tested our tokenizer by performing classification tasks on Korean user-generated movie reviews and hate speech datasets, and the Korean Named Entity Recognition dataset. Through our tests, we found that K-MT is better fit to process internet slangs, proper nouns, and coinages, compared to a morpheme analyzer and a character-level WordPiece tokenizer.
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Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical investigations. However, despite the recent advances in NLP, it is curren
tly unknown if such models are robust in face of negation, which is pervasive across language varieties. In this paper we evaluate three state-of-the-art systems, showing their fragility against negation, and then we introduce two possible strategies to increase the robustness of these models: a pipeline approach, relying on a specific component for negation detection; an augmentation of an ADE extraction dataset to artificially create negated samples and further train the models. We show that both strategies bring significant increases in performance, lowering the number of spurious entities predicted by the models. Our dataset and code will be publicly released to encourage research on the topic.
Morphological analysis (MA) and lexical normalization (LN) are both important tasks for Japanese user-generated text (UGT). To evaluate and compare different MA/LN systems, we have constructed a publicly available Japanese UGT corpus. Our corpus comp
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In most of neural machine translation distillation or stealing scenarios, the highest-scoring hypothesis of the target model (teacher) is used to train a new model (student). If reference translations are also available, then better hypotheses (with
respect to the references) can be oversampled and poor hypotheses either removed or undersampled. This paper explores the sampling method landscape (pruning, hypothesis oversampling and undersampling, deduplication and their combination) with English to Czech and English to German MT models using standard MT evaluation metrics. We show that careful oversampling and combination with the original data leads to better performance when compared to training only on the original or synthesized data or their direct combination.
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