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Register a new userData Augmentation for Cross-Domain Named Entity Recognition
تكبير البيانات لمجال التعرف على الكيان المسمى
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يدل العمل الحالي في التعرف على الكيان المسمى (NER) أن تقنيات تكبير البيانات يمكن أن تنتج نماذج أكثر قوة.ومع ذلك، تركز معظم التقنيات الموجودة على زيادة البيانات داخل المجال في سيناريوهات الموارد المنخفضة حيث تكون البيانات المشروحة محدودة للغاية.في هذا العمل، نأخذ هذا الاتجاه البحثي إلى المعاكس ودراسة تكبير بيانات المجال عبر المجال لمهمة NER.نحن نبحث في إمكانية الاستفادة من البيانات من مجالات الموارد العالية من خلال إسقاطها في مجالات الموارد المنخفضة.على وجه التحديد، نقترح بنية عصبية رواية لتحويل تمثيل البيانات من الموارد العالية إلى مجال موارد منخفضة من خلال تعلم الأنماط (مثل الأناقة والضوضاء والاختصارات، وما إلى ذلك) في النص الذي يميزها ومساحة ميزة مشتركةحيث يتماشى كلا المجالين.نقوم بتجربة مجموعات بيانات متنوعة وإظهار أن تحويل البيانات إلى تمثيل مجال الموارد المنخفض يحقق تحسينات كبيرة على استخدام البيانات فقط من مجالات الموارد العالية.
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 limited. 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.
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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
Cross-domain Named Entity Recognition (NER) transfers the NER knowledge from high-resource domains to the low-resource target domain. Due to limited labeled resources and domain shift, cross-domain NER is a challenging task. To address these challeng
es, we propose a progressive domain adaptation Knowledge Distillation (KD) approach -- PDALN. It achieves superior domain adaptability by employing three components: (1) Adaptive data augmentation techniques, which alleviate cross-domain gap and label sparsity simultaneously; (2) Multi-level Domain invariant features, derived from a multi-grained MMD (Maximum Mean Discrepancy) approach, to enable knowledge transfer across domains; (3) Advanced KD schema, which progressively enables powerful pre-trained language models to perform domain adaptation. Extensive experiments on four benchmarks show that PDALN can effectively adapt high-resource domains to low-resource target domains, even if they are diverse in terms and writing styles. Comparison with other baselines indicates the state-of-the-art performance of PDALN.
Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare entities. Existing approaches are limited by the presence of coarse-grain
ed structural resources in biomedical knowledge bases as well as the use of training datasets that provide low coverage over uncommon resources. In this work, we address these issues by proposing a cross-domain data integration method that transfers structural knowledge from a general text knowledge base to the medical domain. We utilize our integration scheme to augment structural resources and generate a large biomedical NED dataset for pretraining. Our pretrained model with injected structural knowledge achieves state-of-the-art performance on two benchmark medical NED datasets: MedMentions and BC5CDR. Furthermore, we improve disambiguation of rare entities by up to 57 accuracy points.
Named Entity Recognition is an essential task in natural language processing to detect entities and classify them into predetermined categories. An entity is a meaningful word, or phrase that refers to proper nouns. Named Entities play an important r
ole in different NLP tasks such as Information Extraction, Question Answering and Machine Translation. In Machine Translation, named entities often cause translation failures regardless of local context, affecting the output quality of translation. Annotating named entities is a time-consuming and expensive process especially for low-resource languages. One solution for this problem is to use word alignment methods in bilingual parallel corpora in which just one side has been annotated. The goal is to extract named entities in the target language by using the annotated corpus of the source language. In this paper, we compare the performance of two alignment methods, Grow-diag-final-and and Intersect Symmetrisation heuristics, to exploit the annotation projection of English-Brazilian Portuguese bilingual corpus to detect named entities in Brazilian Portuguese. A NER model that is trained on annotated data extracted from the alignment methods, is used to evaluate the performance of aligners. Experimental results show the Intersect Symmetrisation is able to achieve superior performance scores compared to the Grow-diag-final-and heuristic in Brazilian Portuguese.
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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