Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDaNLP: An open-source toolkit for Danish Natural Language Processing
Danlp: مجموعة أدوات مفتوحة المصدر لمعالجة اللغة الطبيعية الدنماركية
732
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
نقدم مجموعة أدوات مفتوحة المصدر لمعالجة اللغة الطبيعية الدنماركية، مما يتيح سهولة الوصول إلى أحدث التطورات الدنماركية ل NLP.يتميز مجموعة الأدوات بوظائف المجمع لتحميل النماذج ومجموعات البيانات بطريقة موحدة باستخدام أطر NLP لجهة خارجية.تم تطوير مجموعة الأدوات لتعزيز بناء المجتمع وفهم الحاجة من تقاسم الصناعة والمعرفة.كمثال على ذلك، نقدم تغريدات غاضبة: لعبة توضيحي لإنشاء وعي NLP الدنماركي وخلق مجموعة بيانات جديدة مشروحة معنويات.
We present an open-source toolkit for Danish Natural Language Processing, enabling easy access to Danish NLP's latest advancements. The toolkit features wrapper-functions for loading models and datasets in a unified way using third-party NLP frameworks. The toolkit is developed to enhance community building, understanding the need from industry and knowledge sharing. As an example of this, we present Angry Tweets: An Annotation Game to create awareness of Danish NLP and create a new sentiment-annotated dataset.
References used
https://aclanthology.org/
rate research
Read More
In this paper we present a new Massive Open Online Course on Natural Language Processing, targeted at non-English speaking students. The course lasts 12 weeks, every week consists of lectures, practical sessions and quiz assigments. Three weeks out o
f 12 are followed by Kaggle-style coding assigments. Our course intents to serve multiple purposes: (i) familirize students with the core concepts and methods in NLP, such as language modelling or word or sentence representations, (ii) show that recent advances, including pre-trained Transformer-based models, are build upon these concepts; (iii) to introduce architectures for most most demanded real-life applications, (iii) to develop practical skills to process texts in multiple languages. The course was prepared and recorded during 2020 and so far have received positive feedback.
Despite its proven efficiency in other fields, data augmentation is less popular in the context of natural language processing (NLP) due to its complexity and limited results. A recent study (Longpre et al., 2020) showed for example that task-agnosti
c data augmentations fail to consistently boost the performance of pretrained transformers even in low data regimes. In this paper, we investigate whether data-driven augmentation scheduling and the integration of a wider set of transformations can lead to improved performance where fixed and limited policies were unsuccessful. Our results suggest that, while this approach can help the training process in some settings, the improvements are unsubstantial. This negative result is meant to help researchers better understand the limitations of data augmentation for NLP.
It is generally agreed upon in the natural language processing (NLP) community that ethics should be integrated into any curriculum. Being aware of and understanding the relevant core concepts is a prerequisite for following and participating in the
discourse on ethical NLP. We here present ready-made teaching material in the form of slides and practical exercises on ethical issues in NLP, which is primarily intended to be integrated into introductory NLP or computational linguistics courses. By making this material freely available, we aim at lowering the threshold to adding ethics to the curriculum. We hope that increased awareness will enable students to identify potentially unethical behavior.
Due to its great power in modeling non-Euclidean data like graphs or manifolds, deep learning on graph techniques (i.e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems. There has seen a surge of
interests in applying deep learning on graph techniques to NLP, and has achieved considerable success in many NLP tasks, ranging from classification tasks like sentence classification, semantic role labeling and relation extraction, to generation tasks like machine translation, question generation and summarization. Despite these successes, deep learning on graphs for NLP still face many challenges, including automatically transforming original text sequence data into highly graph-structured data, and effectively modeling complex data that involves mapping between graph-based inputs and other highly structured output data such as sequences, trees, and graph data with multi-types in both nodes and edges. This tutorial will cover relevant and interesting topics on applying deep learning on graph techniques to NLP, including automatic graph construction for NLP, graph representation learning for NLP, advanced GNN based models (e.g., graph2seq, graph2tree, and graph2graph) for NLP, and the applications of GNNs in various NLP tasks (e.g., machine translation, natural language generation, information extraction and semantic parsing). In addition, hands-on demonstration sessions will be included to help the audience gain practical experience on applying GNNs to solve challenging NLP problems using our recently developed open source library -- Graph4NLP, the first library for researchers and practitioners for easy use of GNNs for various NLP tasks.
دراسة لعدة برمجيات مفتوحة المصدر لإدارة المكتبات الرقمية المستخدمة لاستيعاب المعلومات ونشرها الى الأشخاص الذين يحتاجونها.
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
