A frame-based approach for capturing semantics from Arabic text for text-to-sign language MT

Lakhfif, Abdelaziz; Laskri, Mohamed Tayeb

doi:10.1007/s10772-015-9290-8

Cited by 9 publications

(5 citation statements)

References 68 publications

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“…Ismail et al (2022) presented a new alignment word-space approach for snipped text similarity. Lakhfif and Laskri (2016) described the design and implementation of a computational model for an Arabic semantic parser that could create a deeper semantic representation of Arabic text. They showed that the integration of WordNet and FrameNet can improve disambiguation accuracy.…”

Section: Examples Of How To Pick a Good American President From 2016 ...mentioning

confidence: 99%

Longitudinal Study of a Website for Assessing American Presidential Candidates and Decision Making of Potential Election Irregularities Detection

Piper

Rodger

2022

International Journal on Semantic Web and Information Systems

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We employ the concept of word sense disambiguation to determine the inherent meaning of voter intentions regarding possible political candidates from the 2016 Presidential election. We present our findings based on a website (www.presidentselect.com) that we developed, where candidates can be examined and their true assets and competencies in three major areas of eligibility, education, and experience inputs can be deciphered. Data envelope analysis is used to determine underlying word instances for elected and successful outputs. We also utilize our web site results to longitudinally extend these findings for decision making of potential election fraud detection in the 2020 Presidential election, utilizing Benford’s Law. Our results shed light on these phenomenon and provide new insights into the word sense disambiguation literature.

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Section: Examples Of How To Pick a Good American President From 2016 ...mentioning

confidence: 99%

Longitudinal Study of a Website for Assessing American Presidential Candidates and Decision Making of Potential Election Irregularities Detection

Piper

Rodger

2022

International Journal on Semantic Web and Information Systems

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“…FrameNet (FN) was developed in an effort to build a lexicon of English that is comprehendible by both human and machine, using the theory of frame semantics and backed by means of an annotated corpus of lexical items (Baker, 2014;Fillmore et al, 2003;Lakhfif & Laskri, 2015). The knowledge base structure of FN is defined as a relation between frames at various levels of generality.…”

Section: Framenetmentioning

confidence: 99%

“…The annotation process involves FN lexicographers to declare each word in a sentence as a target, then select a frame related to the target, get a set of annotation layers and appropriate frame element tags and the annotate the relevant constituents (Ruppenhofer et al, 2006). FN version 1.3, is a freely available lexical database which contains a wealth of semantic knowledge of about 1161 Semantic Frames, covering more than 12,600 lexical units, documented with nearly 200,000 manual annotations (Lakhfif & Laskri, 2015). FN is developed based on the eXtensible Markup Language (XML) (Martínez-Santiago et al, 2015).…”

Section: Framenetmentioning

confidence: 99%

Towards knowledge modeling and manipulation technologies: A survey

Bimba

Al-Hunaiyyan

Mahmud

et al. 2016

International Journal of Information Management

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A system which represents knowledge is normally referred to as a knowledge based system (KBS). This article focuses on surveying publications related to knowledge base modelling and manipulation technologies, between the years 2000-2015. A total of 185 articles excluding the subject descriptive articles which are mentioned in the introductory parts, were evaluated in this survey. The main aim of this study is to identify different knowledge base modelling and manipulation techniques based on 4 categories; 1) linguistic knowledge base; 2) expert knowledge base; 3) ontology and 4) cognitive knowledge base. This led to the proposition of 8 research questions, which focused on the different categories of knowledge base modelling technologies, their underlying theories, knowledge representation technique, knowledge acquisition technique, challenges, applications, development tools and development languages. A part of the findings from this survey is the high dependence of linguistic knowledge base, expert knowledge base and ontology on volatile expert knowledge. A promising technique for knowledge-based business management and other knowledge related applications is also discussed.

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“…For Arabic video text recognition, this paper uses the same sequence to sequence model. A great deal of work has been carried out in [16,17] to recognize video text. The availability of Arabic script datasets is minor compared to English script datasets, but for video text recognition, two datasets are available: the ACTIV dataset [18] and the ALIF dataset [19].…”

Section: Introductionmentioning

confidence: 99%

Attention-Based CNN-RNN Arabic Text Recognition from Natural Scene Images

et al. 2021

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According to statistics, there are 422 million speakers of the Arabic language. Islam is the second-largest religion in the world, and its followers constitute approximately 25% of the world’s population. Since the Holy Quran is in Arabic, nearly all Muslims understand the Arabic language per some analytical information. Many countries have Arabic as their native and official language as well. In recent years, the number of internet users speaking the Arabic language has been increased, but there is very little work on it due to some complications. It is challenging to build a robust recognition system (RS) for cursive nature languages such as Arabic. These challenges become more complex if there are variations in text size, fonts, colors, orientation, lighting conditions, noise within a dataset, etc. To deal with them, deep learning models show noticeable results on data modeling and can handle large datasets. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can select good features and follow the sequential data learning technique. These two neural networks offer impressive results in many research areas such as text recognition, voice recognition, several tasks of Natural Language Processing (NLP), and others. This paper presents a CNN-RNN model with an attention mechanism for Arabic image text recognition. The model takes an input image and generates feature sequences through a CNN. These sequences are transferred to a bidirectional RNN to obtain feature sequences in order. The bidirectional RNN can miss some preprocessing of text segmentation. Therefore, a bidirectional RNN with an attention mechanism is used to generate output, enabling the model to select relevant information from the feature sequences. An attention mechanism implements end-to-end training through a standard backpropagation algorithm.

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A frame-based approach for capturing semantics from Arabic text for text-to-sign language MT

Cited by 9 publications

References 68 publications

Longitudinal Study of a Website for Assessing American Presidential Candidates and Decision Making of Potential Election Irregularities Detection

Longitudinal Study of a Website for Assessing American Presidential Candidates and Decision Making of Potential Election Irregularities Detection

Towards knowledge modeling and manipulation technologies: A survey

Attention-Based CNN-RNN Arabic Text Recognition from Natural Scene Images

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