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Register a new userPartially Supervised Named Entity Recognition via the Expected Entity Ratio Loss
تحت إشراف جزئيا على الكيان التعرف على فقدان نسبة الكيان المتوقعة
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الملخص ندرس التعلم التعرف على الكيان المسمى في وجود التعليقات التوضيحية في الكيان المفقود.نحن نقترب من هذا الإعداد باسم وضع علامات مع المتغيرات الكامنة واقتراح خسارة جديدة، ونسبة الكيان المتوقعة، لتعلم النماذج بحضور العلامات المفقودة بشكل منهجي.نظرا لأن نهجنا صوتي من الناحية النظرية ومفيدة تجريبيا.تجريفيا، نجد أنه يجتمع أو يتجاوز أداء خطوط أساس قوية وحديثة من بين الفنون عبر مجموعة متنوعة من اللغات والسيناريوهات التوضيحية ومبالغ البيانات المسمى.على وجه الخصوص، نجد أنه يتفوق بشكل كبير على الطرق السابقة من الأساليب السابقة من Mayhew et al.(2019) ولي وآخرون.(2021) بواسطة +12.7 و +2.3 F1 النتيجة في بيئة صعبة مع فقط 1000 عرض توضيحية متحيزة، بلغ متوسطها عبر 7 مجموعات من مجموعات البيانات.نظهر أيضا أنه عندما يقترن نهجنا، فإن مخطط التعليق التوضيحي رواية متفوقة تفوق التعليق التوضيحي الشامل لميزانيات التوضيحية المتواضعة
Abstract We study learning named entity recognizers in the presence of missing entity annotations. We approach this setting as tagging with latent variables and propose a novel loss, the Expected Entity Ratio, to learn models in the presence of systematically missing tags. We show that our approach is both theoretically sound and empirically useful. Experimentally, we find that it meets or exceeds performance of strong and state-of-the-art baselines across a variety of languages, annotation scenarios, and amounts of labeled data. In particular, we find that it significantly outperforms the previous state-of-the-art methods from Mayhew et al. (2019) and Li et al. (2021) by +12.7 and +2.3 F1 score in a challenging setting with only 1,000 biased annotations, averaged across 7 datasets. We also show that, when combined with our approach, a novel sparse annotation scheme outperforms exhaustive annotation for modest annotation budgets.1
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Entity Linking (EL) systems have achieved impressive results on standard benchmarks mainly thanks to the contextualized representations provided by recent pretrained language models. However, such systems still require massive amounts of data -- mill
ions of labeled examples -- to perform at their best, with training times that often exceed several days, especially when limited computational resources are available. In this paper, we look at how Named Entity Recognition (NER) can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data. More specifically, we show how and to what extent an EL system can benefit from NER to enhance its entity representations, improve candidate selection, select more effective negative samples and enforce hard and soft constraints on its output entities. We release our software -- code and model checkpoints -- at https://github.com/Babelscape/ner4el.
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
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.
Nowadays, named entity recognition (NER) achieved excellent results on the standard corpora. However, big issues are emerging with a need for an application in a specific domain, because it requires a suitable annotated corpus with adapted NE tag-set
. This is particularly evident in the historical document processing field. The main goal of this paper consists of proposing and evaluation of several transfer learning methods to increase the score of the Czech historical NER. We study several information sources, and we use two neural nets for NE modeling and recognition. We employ two corpora for evaluation of our transfer learning methods, namely Czech named entity corpus and Czech historical named entity corpus. We show that BERT representation with fine-tuning and only the simple classifier trained on the union of corpora achieves excellent results.
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.
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