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Register a new userIsolated Word Recognition
التعرف على الكلمات المعزولة
2017
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Tishreen University مشروع تخرج
Publication date
2016
fields
Informatics Engineering
and research's language is
العربية
Created by
Zulfiqar Murhej
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الغاية من هذا البحث بناء نظام لتصنيف نطق الأرقام الانكليزية وذلك بالاعتماد على نماذج ماركوف المخفية في التصنيف وذلك بالاعتماد على طيف الإشارة في استخراج سمات الإشارات
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Fundamentals of Speech Recognition
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This article describes a system to predict the complexity of words for the Lexical Complexity Prediction (LCP) shared task hosted at SemEval 2021 (Task 1) with a new annotated English dataset with a Likert scale. Located in the Lexical Semantics trac
k, the task consisted of predicting the complexity value of the words in context. A machine learning approach was carried out based on the frequency of the words and several characteristics added at word level. Over these features, a supervised random forest regression algorithm was trained. Several runs were performed with different values to observe the performance of the algorithm. For the evaluation, our best results reported a M.A.E score of 0.07347, M.S.E. of 0.00938, and R.M.S.E. of 0.096871. Our experiments showed that, with a greater number of characteristics, the precision of the classification increases.
To evaluate the symptoms, causes, methods of management and efficacy of this
methods of isolated sphenoid sinus disease.
Isolated sphenoid sinus disease is uncommon, but it may be critical and a cause of important
ocular and neurological complications.
Modern natural language understanding models depend on pretrained subword embeddings, but applications may need to reason about words that were never or rarely seen during pretraining. We show that examples that depend critically on a rarer word are
more challenging for natural language inference models. Then we explore how a model could learn to use definitions, provided in natural text, to overcome this handicap. Our model's understanding of a definition is usually weaker than a well-modeled word embedding, but it recovers most of the performance gap from using a completely untrained word.
In this work, our goal is recognizing human action from video data. First we
propose an overview about Human Action Recognition, includes the famous
methods and previous algorithms, then we propose an algorithm and its
implementation using MATLAB.
Abstract We take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition
(NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state- of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.1
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