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Register a new userTowards the Addition of Pronunciation Information to Lexical Semantic Resources
نحو إضافة معلومات النطق إلى الموارد الدلالية المعجمية
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تصف هذه الورقة العمل المستمر الذي يهدف إلى إضافة معلومات النطق إلى الموارد الدلالية المعجمية، مع التركيز على WordNets المفتوح.هدفنا ليس فقط لإضافة طريقة جديدة إلى تلك الشبكات الدلالية، ولكن أيضا لعلامة الجوطاء المدرجة فيها مع معلومات النطق المرتبطة بمعاني مختلفة.هذا العمل يمكن أن يسهم على المدى الطويل لزيادة الموارد متعددة الوسائط، والذي يجمع بين النص والكلام.
This paper describes ongoing work aiming at adding pronunciation information to lexical semantic resources, with a focus on open wordnets. Our goal is not only to add a new modality to those semantic networks, but also to mark heteronyms listed in them with the pronunciation information associated with their different meanings. This work could contribute in the longer term to the disambiguation of multi-modal resources, which are combining text and speech.
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The SemLink resource provides mappings between a variety of lexical semantic ontologies, each with their strengths and weaknesses. To take advantage of these differences, the ability to move between resources is essential. This work describes advance
s made to improve the usability of the SemLink resource: the automatic addition of new instances and mappings, manual corrections, sense-based vectors and collocation information, and architecture built to automatically update the resource when versions of the underlying resources change. These updates improve coverage, provide new tools to leverage the capabilities of these resources, and facilitate seamless updates, ensuring the consistency and applicability of these mappings in the future.
يهدف هذا البحث إلى تقييم استخدام الحاسب الآلي في نظم معلومات الموارد البشرية في
جامعة تشرين، من خلال قياس استخدام الحاسوب في مراحل الإدخال و الإخراج و المعالجة،
حيث تم توزيع استبانة على عينة من الموظفين العاملين في مكاتب إدارة الموارد البشرية في
ا
لكليات و الإدارة المركزية في جامعة تشرين بلغت 88 موظفا، و تم إجراء توصيف للمتغيرات
الديمغرافية، و بلغ معامل إلفا كرونباخ 0.828 مما يدل على ثبات المقياس، و خلصت الدراسة
إلى انخفاض معدل استخدام الحاسب الآلي في نظم معلومات الموارد، في كل من الإدخال
و التخزين، و الإخراج، و العمليات (تخطيط الموارد البشرية، التقييم و ادارة الأداء، الاستقطاب
و التعيين، التدريب و التطوير، الاتصال و التواصل)، في حين وجد الباحث استخدام للحاسب
الآلي في عمليات إعداد الرواتب و الأجور، و اعتمد الباحث عمى اختبار T ستيودينت لعينة
واحدة باستخدام برنامج SPSS بالإصدار رقم 20 لاختبار الفرضيات المعتمدة في البحث.
Pronunciation lexicons and prediction models are a key component in several speech synthesis and recognition systems. We know that morphologically related words typically follow a fixed pattern of pronunciation which can be described by language-spec
ific paradigms. In this work we explore how deep recurrent neural networks can be used to automatically learn and exploit this pattern to improve the pronunciation prediction quality of words related by morphological inflection. We propose two novel approaches for supplying morphological information, using the word's morphological class and its lemma, which are typically annotated in standard lexicons. We report improvements across a number of European languages with varying degrees of phonological and morphological complexity, and two language families, with greater improvements for languages where the pronunciation prediction task is inherently more challenging. We also observe that combining bidirectional LSTM networks with attention mechanisms is an effective neural approach for the computational problem considered, across languages. Our approach seems particularly beneficial in the low resource setting, both by itself and in conjunction with transfer learning.
While powerful pre-trained language models have improved the fluency of text generation models, semantic adequacy -the ability to generate text that is semantically faithful to the input- remains an unsolved issue. In this paper, we introduce a novel
automatic evaluation metric, Entity-Based Semantic Adequacy, which can be used to assess to what extent generation models that verbalise RDF (Resource Description Framework) graphs produce text that contains mentions of the entities occurring in the RDF input. This is important as RDF subject and object entities make up 2/3 of the input. We use our metric to compare 25 models from the WebNLG Shared Tasks and we examine correlation with results from human evaluations of semantic adequacy. We show that while our metric correlates with human evaluation scores, this correlation varies with the specifics of the human evaluation setup. This suggests that in order to measure the entity-based adequacy of generated texts, an automatic metric such as the one proposed here might be more reliable, as less subjective and more focused on correct verbalisation of the input, than human evaluation measures.
This study aims to identify the impact of the use of information
technology to develop and improve the performance of human
resources at all the different levels of management in an
organization and the impact on job performance.
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