Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDynamic Ensembles in Named Entity Recognition for Historical Arabic Texts
الفرم الديناميكي في التعرف على الكيان المسمى للنصوص العربية التاريخية
328
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
يتزايد استخدام التعرف على الكيان المسمى (NER) على النصوص العربية القديمة بشكل مطرد.ومع ذلك، فقد تم تطوير معظم الأدوات لإرجاع اللغة الإنجليزية الحديثة أو تدربت على وثائق اللغة الإنجليزية وهي محدودة للنص العربي التاريخي.حتى أدوات NER العربية غالبا ما تدرب على نص حديث من مصادر الويب، مما يجعل مناسبا له بمهمة تاريخية مشكوك فيها.لتخفيف ندرة الموارد العربية السعودية العربية، نقترح نموذج فرقة ديناميكية باستخدام العديد من المتعلمين.يتم تحقيق الجانب الديناميكي من خلال الاستفادة من التنبؤ والميزات على نتائج خوارزمية NER التي حددت التي أجريت بشكل أفضل على مهمة محددة في الوقت الفعلي.نقوم بتقييم نهجنا ضد أحدث أساليب النيران العربية والثابتة من أساليب الفرقة الثابتة عبر مهمة تاريخية تاريخية جديدة التي أنشأناها.تظهر نتائجنا أن نهجنا يحسن على أحدث ويودر من 0.8 درجة مئوية بشأن هذه المهمة الصعبة.
The use of Named Entity Recognition (NER) over archaic Arabic texts is steadily increasing. However, most tools have been either developed for modern English or trained over English language documents and are limited over historical Arabic text. Even Arabic NER tools are often trained on modern web-sourced text, making their fit for a historical task questionable. To mitigate historic Arabic NER resource scarcity, we propose a dynamic ensemble model utilizing several learners. The dynamic aspect is achieved by utilizing predictors and features over NER algorithm results that identify which have performed better on a specific task in real-time. We evaluate our approach against state-of-the-art Arabic NER and static ensemble methods over a novel historical Arabic NER task we have created. Our results show that our approach improves upon the state-of-the-art and reaches a 0.8 F-score on this challenging task.
References used
https://aclanthology.org/
rate research
Read More
Pretrained language models like BERT have advanced the state of the art for many NLP tasks. For resource-rich languages, one has the choice between a number of language-specific models, while multilingual models are also worth considering. These mode
ls are well known for their crosslingual performance, but have also shown competitive in-language performance on some tasks. We consider monolingual and multilingual models from the perspective of historical texts, and in particular for texts enriched with editorial notes: how do language models deal with the historical and editorial content in these texts? We present a new Named Entity Recognition dataset for Dutch based on 17th and 18th century United East India Company (VOC) reports extended with modern editorial notes. Our experiments with multilingual and Dutch pretrained language models confirm the crosslingual abilities of multilingual models while showing that all language models can leverage mixed-variant data. In particular, language models successfully incorporate notes for the prediction of entities in historical texts. We also find that multilingual models outperform monolingual models on our data, but that this superiority is linked to the task at hand: multilingual models lose their advantage when confronted with more semantical tasks.
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
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.
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.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
