An Environment for Named Entity Recognition and TranslationKrzysztof Jassem
Although there exists a huge number of biomedical texts online, there is a lack of tools good enough to help people get information or knowledge from them. Named entity Recognition (NER) becomes very important for further processing like information retrieval, information extraction and knowledge disc...
过去的命名实体识别主要考虑的是扁平命名实体识别(flat NER)。近年来有一些工作考虑了实体之间可能存在嵌套关系,由此对应地提出嵌套命名实体识别(nested NER)这一新任务。 具体来说,如下图(1)所示:“上海市红十字会”就是一个典型的包含嵌套命名实体的例子。其中“上海市”是地名、“红十字会”是组织名、“上海市红...
In the process of named entity recognition task evaluation, the main evaluation metrics are also Precision, Recall, and F-value. 4.6 Annotation tools One AI28 is an online platform that offers NLP-as-a-service. The utilization of APIs enables developers to effectively analyze, manipulate, and ...
6.8.3Named Entity Normalization Given some semantic class of a named entity, it may not be sufficient simply to recognize when one is mentioned in text (the named entity recognition task). Rather, it is often desirable or necessary to map that named entity to some entity in a database, or...
Meta-Learning with Selective Data Augmentation for Medical Entity Recognition With the increasing number of annotated corpora for super- vised Named Entity Recognition, it becomes interesting to study the combination and augmentation... ASMA BEN ABACHA,DINA DEMNER-FUSHMAN - 《International Journal of Co...
Multi-task learning (MTL) has led to successes in many applications of machine learning, from natural language processing and speech recognition to computer vision and drug discovery. This article aims to give a general overview of MTL, particularly in deep neural networks. It introduces the two ...
The diagram below presents an overview of NeuroNER. NeuroNER can be used as follows: Train the neural network that performs the NER. During the training, NeuroNER allows to monitor the network Evaluate the quality of the predictions made by NeuroNER. The performance metrics can be calculated and...
9886 DUST: DUAL-GRAINED SYNTAX-AWARE TRANSFORMER NETWORK FOR CHINESE NAMED ENTITY RECOGNITION 8312 Dynamic ASR pathways: An Adaptive Masking Approach Towards Efficient Pruning of a Multilingual ASR Model 5556 DYNAMIC BANDWIDTH VARIATIONAL MODE DECOMPOSITION 5597 Dynamic Clustering and Cluster Contrastive Learn...
Few-Shot Named Entity Recognition: An Empirical Baseline Study 2021 emnlp Abstract 本文提出了一项实证研究,当有少量的域内有标记数据时,可以有效地建立命名实体识别系统。基于近来的基于Transformer的自监督预训练语言模型(PLMs),本文研究了三种正交的方案来提高模型在少数情况下的泛化能力:(1)使用元学习来构建不同...