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Register a new userBetter Feature Integration for Named Entity Recognition
أفضل ميزة التكامل لمعرفة الكيان المسمى
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وقد ثبت أن التعرف على الكيان المسمى (NER) قد يستفيد من دمج المعلومات المهيكلة لمسافات طويلة التي تم التقاطها بواسطة أشجار التبعية. نعتقد أن هذا هو أن كلا النوعين من الميزات - المعلومات السياقية التي تم التقاطها من خلال التسلسلات الخطية والمعلومات المهيكلة التي تم الاستيلاء عليها أشجار التبعية قد تكمل بعضها البعض. ومع ذلك، تركز النهج الحالية إلى حد كبير على تكديس الشبكات العصبية LSTM والرسم البيانية مثل الشبكات التنافسية الرسم البيانية (GCNS) لبناء نماذج NER محسنة، حيث لا تكون آلية التفاعل الدقيقة بين النوعين من الميزات واضحة للغاية، وتكسب الأداء يبدو أن تكون كبيرة. في هذا العمل، نقترح حلا بسيطا وقويا لدمج كلا النوعين من الميزات مع تآزرنا - LSTM (SYN-LSTM)، والذي يلتقط بوضوح كيف يتفاعل نوعان الميزات. نقوم بإجراء تجارب مكثفة على العديد من البيانات القياسية عبر أربع لغات. تظهر النتائج أن النموذج المقترح يحقق أداء أفضل من الأساليب السابقة مع مطالبة معلمات أقل. يوضح تحليلنا الإضافي أن نموذجنا يمكنه التقاط تبعيات أطول مقارنة مع خطوط الأساس القوية.
It has been shown that named entity recognition (NER) could benefit from incorporating the long-distance structured information captured by dependency trees. We believe this is because both types of features - the contextual information captured by the linear sequences and the structured information captured by the dependency trees may complement each other. However, existing approaches largely focused on stacking the LSTM and graph neural networks such as graph convolutional networks (GCNs) for building improved NER models, where the exact interaction mechanism between the two types of features is not very clear, and the performance gain does not appear to be significant. In this work, we propose a simple and robust solution to incorporate both types of features with our Synergized-LSTM (Syn-LSTM), which clearly captures how the two types of features interact. We conduct extensive experiments on several standard datasets across four languages. The results demonstrate that the proposed model achieves better performance than previous approaches while requiring fewer parameters. Our further analysis demonstrates that our model can capture longer dependencies compared with strong baselines.
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Transformer models fine-tuned with a sequence labeling objective have become the dominant choice for named entity recognition tasks. However, a self-attention mechanism with unconstrained length can fail to fully capture local dependencies, particula
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Abstract We take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition
(NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state- of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.1
Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-domain data in low-resource scenarios where annotated data is quite limite
d. In this work, we take this research direction to the opposite and study cross-domain data augmentation for the NER task. We investigate the possibility of leveraging data from high-resource domains by projecting it into the low-resource domains. Specifically, we propose a novel neural architecture to transform the data representation from a high-resource to a low-resource domain by learning the patterns (e.g. style, noise, abbreviations, etc.) in the text that differentiate them and a shared feature space where both domains are aligned. We experiment with diverse datasets and show that transforming the data to the low-resource domain representation achieves significant improvements over only using data from high-resource domains.
Although pre-trained big models (e.g., BERT, ERNIE, XLNet, GPT3 etc.) have delivered top performance in Seq2seq modeling, their deployments in real-world applications are often hindered by the excessive computations and memory demand involved. For ma
ny applications, including named entity recognition (NER), matching the state-of-the-art result under budget has attracted considerable attention. Drawing power from the recent advance in knowledge distillation (KD), this work presents a novel distillation scheme to efficiently transfer the knowledge learned from big models to their more affordable counterpart. Our solution highlights the construction of surrogate labels through the k-best Viterbi algorithm to distill knowledge from the teacher model. To maximally assimilate knowledge into the student model, we propose a multi-grained distillation scheme, which integrates cross entropy involved in conditional random field (CRF) and fuzzy learning.To validate the effectiveness of our proposal, we conducted a comprehensive evaluation on five NER benchmarks, reporting cross-the-board performance gains relative to competing prior-arts. We further discuss ablation results to dissect our gains.
We explore the application of state-of-the-art NER algorithms to ASR-generated call center transcripts. Previous work in this domain focused on the use of a BiLSTM-CRF model which relied on Flair embeddings; however, such a model is unwieldy in terms
of latency and memory consumption. In a production environment, end users require low-latency models which can be readily integrated into existing pipelines. To that end, we present two different models which can be utilized based on the latency and accuracy requirements of the user. First, we propose a set of models which utilize state-of-the-art Transformer language models (RoBERTa) to develop a high-accuracy NER system trained on custom annotated set of call center transcripts. We then use our best-performing Transformer-based model to label a large number of transcripts, which we use to pretrain a BiLSTM-CRF model and further fine-tune on our annotated dataset. We show that this model, while not as accurate as its Transformer-based counterpart, is highly effective in identifying items which require redaction for privacy law compliance. Further, we propose a new general annotation scheme for NER in the call-center environment.
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