[PDF][PDF] Aksharantar: Towards building open transliteration tools for the next billion users
We introduce Aksharantar, the largest publicly available transliteration dataset for 21 Indic
languages containing 26 million transliteration pairs. We build this dataset by mining …
languages containing 26 million transliteration pairs. We build this dataset by mining …
Aksharantar: Open Indic-language transliteration datasets and models for the next billion users
Transliteration is very important in the Indian language context due to the usage of multiple
scripts and the widespread use of romanized inputs. However, few training and evaluation …
scripts and the widespread use of romanized inputs. However, few training and evaluation …
[PDF][PDF] Arabic named entity recognition: A BERT-BGRU approach
N Alsaaran, M Alrabiah - Comput. Mater. Contin, 2021 - academia.edu
Named Entity Recognition (NER) is one of the fundamental tasks in Natural Language
Processing (NLP), which aims to locate, extract, and classify named entities into a …
Processing (NLP), which aims to locate, extract, and classify named entities into a …
Improving NER tagging performance in low-resource languages via multilingual learning
Existing supervised solutions for Named Entity Recognition (NER) typically rely on a large
annotated corpus. Collecting large amounts of NER annotated corpus is time-consuming …
annotated corpus. Collecting large amounts of NER annotated corpus is time-consuming …
Judicious selection of training data in assisting language for multilingual neural NER
Abstract Multilingual learning for Neural Named Entity Recognition (NNER) involves jointly
training a neural network for multiple languages. Typically, the goal is improving the NER …
training a neural network for multiple languages. Typically, the goal is improving the NER …
An improved word representation for deep learning based NER in Indian languages
Named Entity Recognition (NER) is the process of identifying the elementary units in a text
document and classifying them into predefined categories such as person, location …
document and classifying them into predefined categories such as person, location …
[PDF][PDF] 理论术语抽取的深度学**模型及自训练算法研究
赵洪， 王芳 - 情报学报, 2018 - qbxb.istic.ac.cn
摘要理论术语的抽取是大规模文献内容分析和跨学科知识转移深度揭示的基础.
作为一种特定类型的命名实体, 理论术语涉及的学科多, 文献规模大, 特征复杂 …
作为一种特定类型的命名实体, 理论术语涉及的学科多, 文献规模大, 特征复杂 …
Bidirectional lstm-cnns with extended features for named entity recognition
N Bölücü, D Akgöl, S Tuç - 2019 Scientific Meeting on Electrical …, 2019 - ieeexplore.ieee.org
Named Entity Recognition (NER) is vital preprocessing step for many Natural Language
Processing applications such as relation extraction and question answering. NER has been …
Processing applications such as relation extraction and question answering. NER has been …
Quality of word vectors and its impact on named entity recognition in czech
F Dařena, M Süss - European Journal of Business Science …, 2020 - repozitar.mendelu.cz
Named Entity Recognition (NER) focuses on finding named entities in text and classifying
them into one of the entity types. Modern state-of-the-art NER approaches avoid using hand …
them into one of the entity types. Modern state-of-the-art NER approaches avoid using hand …
[PDF][PDF] Named entity recognition using deep learning
R Murthy - 14th international conference on natural language …, 2017 - iitp.ac.in
Named Entity Recognition Using Deep Learning Page 1 Named Entity Recognition Using Deep
Learning Rudra Murthy Center for Indian Language Technology, Indian Institute of Technology …
Learning Rudra Murthy Center for Indian Language Technology, Indian Institute of Technology …