لترجيل اللغة المنطوقة إلى المتوسطة المكتوبة، تمكن معظم الحروف الهجائية قاعدة صوتية لا لبس فيها.ومع ذلك، فقد نأت بعض أنظمة الكتابة أنفسهم من هذا المفهوم البسيط والعمل القليل من العمل في معالجة اللغة الطبيعية (NLP) على قياس المسافة.في هذه الدراسة، نستخدم نموذج شبكة عصبي اصطناعي (آن) لتقييم الشفافية بين الكلمات المكتوبة ونطقها، وبالتالي تسميته تقدير الشفافية الذاتية مع آن (Oteann).بناء على مجموعات البيانات المستمدة من قواميس ويكيميديا، ندربنا هذا النموذج واختبر هذا النموذج لتسجيل النسبة المئوية للتنبؤات الخاطئة في مهام الترجمة من PhoneMe-to-grapheme و grapheme-to-phoneme.كانت الدرجات التي تم الحصول عليها على 17 تقييدا تتماشى مع تقديرات الدراسات الأخرى.ومن المثير للاهتمام، أن النموذج قدم أيضا نظرة ثاقبة أخطاء نموذجية مصنوعة من المتعلمين الذين ينظرون فقط في الحكم الصوتي في القراءة والكتابة.
To transcribe spoken language to written medium, most alphabets enable an unambiguous sound-to-letter rule. However, some writing systems have distanced themselves from this simple concept and little work exists in Natural Language Processing (NLP) on measuring such distance. In this study, we use an Artificial Neural Network (ANN) model to evaluate the transparency between written words and their pronunciation, hence its name Orthographic Transparency Estimation with an ANN (OTEANN). Based on datasets derived from Wikimedia dictionaries, we trained and tested this model to score the percentage of false predictions in phoneme-to-grapheme and grapheme-to-phoneme translation tasks. The scores obtained on 17 orthographies were in line with the estimations of other studies. Interestingly, the model also provided insight into typical mistakes made by learners who only consider the phonemic rule in reading and writing.
References used
https://aclanthology.org/
The evaporation is one of the basic components of the hydrologic cycle and it is
essential for studies such as water balance, irrigation system design and water resource
management, and it requires knowledge of many climatic variables. Although, th
This study includes the possibility of using Artificial neural
networks (ANNs) with back-propagation algorithm in a short-term
prediction of water level in Qattinah Lake. The data used are the
water level data in the lake and rainfall data for the period from
1/5/2007 to 28/2/2005. 2009).
The purpose of this article is to shed light on the mechanism
and the procedures of a program that classifies an input face into
any of the six basic facial expressions, which are Anger, Disgust,
Fear, Happiness, Sadness and Surprise, in addition
Rainfall is highly non-linear and complicated phenomena, which require nonlinear
mathematical modeling and simulation for accurate prediction. This study
comparing the performance of the prediction of one-day-ahead, where Two
Feed Forward Neural N
Emotion recognition in multi-party conversation (ERMC) is becoming increasingly popular as an emerging research topic in natural language processing. Prior research focuses on exploring sequential information but ignores the discourse structures of c