scholarly journals Analysis of Japanese-Japanese Sign Language Dictionary Using Natural Language Processing

2008 ◽  
Vol 17 (0) ◽  
pp. 85-92
Author(s):  
Tomohiro Kuroda ◽  
Kazuya Okamoto ◽  
Tadamasa Takemura ◽  
Naoki Oboshi ◽  
Yoshihiro Kuroda ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sign Language ◽  
Language Processing ◽  
Japanese Sign Language

Deep Natural Language Processing for Italian Sign Language Translation

2013 ◽  
pp. 193-204 ◽  
Author(s):  
Alessandro Mazzei ◽  
Leonardo Lesmo ◽  
Cristina Battaglino ◽  
Mara Vendrame ◽  
Monica Bucciarelli
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sign Language ◽  
Language Processing ◽  
Language Translation

scholarly journals Braille, Sign Language and Natural Language Processing

2011 ◽  
Vol 4 (4) ◽  
pp. 282-292
Author(s):  
Takashi IKEDA ◽  
Tadahiro MATSUMOTO
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sign Language ◽  
Language Processing

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence
Sign in / Sign up

Export Citation Format

Share Document