The Experts below are selected from a list of 103419 Experts worldwide ranked by ideXlab platform
Steve Renals - One of the best experts on this subject based on the ideXlab platform.
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Acoustic data-driven pronunciation Lexicon for large vocabulary speech recognition
2013 IEEE Workshop on Automatic Speech Recognition and Understanding ASRU 2013 - Proceedings, 2013Co-Authors: Liang Lu, Arnab Ghoshal, Steve RenalsAbstract:Speech recognition systems normally use handcrafted pronunciation Lexicons designed by linguistic experts. Building and maintaining such a Lexicon is expensive and time consuming. This paper concerns automatically learning a pronunciation Lexicon for speech recognition. We assume the availability of a small seed Lexicon and then learn the pronunciations of new words directly from speech that is transcribed at word-level. We present two implementations for refining the putative pronunciations of new words based on acoustic evidence. The first one is an expectation maximization (EM) algorithm based on weighted finite state transducers (WFSTs) and the other is its Viterbi approximation. We carried out experiments on the Switchboard corpus of conversational telephone speech. The expert Lexicon has a size of more than 30,000 words, from which we randomly selected 5,000 words to form the seed Lexicon. By using the proposed Lexicon learning method, we have significantly improved the accuracy compared with a Lexicon learned using a grapheme-to-phoneme transformation, and have obtained a word error rate that approaches that achieved using a fully handcrafted Lexicon.
Wassim Elhajj - One of the best experts on this subject based on the ideXlab platform.
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a large scale arabic sentiment Lexicon for arabic opinion mining
Empirical Methods in Natural Language Processing, 2014Co-Authors: Gilbert Badaro, Ramy Baly, Hazem Hajj, Nizar Habash, Wassim ElhajjAbstract:Most opinion mining methods in English rely successfully on sentiment Lexicons, such as English SentiWordnet (ESWN). While there have been efforts towards building Arabic sentiment Lexicons, they suffer from many deficiencies: limited size, unclear usability plan given Arabic’s rich morphology, or nonavailability publicly. In this paper, we address all of these issues and produce the first publicly available large scale Standard Arabic sentiment Lexicon (ArSenL) using a combination of existing resources: ESWN, Arabic WordNet, and the Standard Arabic Morphological Analyzer (SAMA). We compare and combine two methods of constructing this Lexicon with an eye on insights for Arabic dialects and other low resource languages. We also present an extrinsic evaluation in terms of subjectivity and sentiment analysis.
Liang Lu - One of the best experts on this subject based on the ideXlab platform.
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Acoustic data-driven pronunciation Lexicon for large vocabulary speech recognition
2013 IEEE Workshop on Automatic Speech Recognition and Understanding ASRU 2013 - Proceedings, 2013Co-Authors: Liang Lu, Arnab Ghoshal, Steve RenalsAbstract:Speech recognition systems normally use handcrafted pronunciation Lexicons designed by linguistic experts. Building and maintaining such a Lexicon is expensive and time consuming. This paper concerns automatically learning a pronunciation Lexicon for speech recognition. We assume the availability of a small seed Lexicon and then learn the pronunciations of new words directly from speech that is transcribed at word-level. We present two implementations for refining the putative pronunciations of new words based on acoustic evidence. The first one is an expectation maximization (EM) algorithm based on weighted finite state transducers (WFSTs) and the other is its Viterbi approximation. We carried out experiments on the Switchboard corpus of conversational telephone speech. The expert Lexicon has a size of more than 30,000 words, from which we randomly selected 5,000 words to form the seed Lexicon. By using the proposed Lexicon learning method, we have significantly improved the accuracy compared with a Lexicon learned using a grapheme-to-phoneme transformation, and have obtained a word error rate that approaches that achieved using a fully handcrafted Lexicon.
Nur Faidatun Naimah - One of the best experts on this subject based on the ideXlab platform.
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motivational Lexicon in anthony robbins unlimited power for pedagogical field psychological perspective
2015Co-Authors: Nur Faidatun NaimahAbstract:Motivators have great power in their Lexicon and beable to influence people to take action for achieving their excellent life. It is like Anthony Robbins with his book; Unlimited Power. In conducting the research, researcher formulated two problems; (1) how are motivational Lexicons in Unlimited Power analyzed from the psychological perspective, and (2) how can motivational Lexicons in Unlimited Power which are analyzed from psychological perspective take apart in the pedagogical field. This research used qualitativedescriptive and documentation as data collection technique. The data analysis technique that researcher used was content analysis since they were texts in Unlimited Power. In conducting this research, researcher used some tables to categorize chapter, page, data, and analysis, then analyzed by using psychological perspective and bringing the analysis into pedagogical field. The main tool for this research is psychological dictionary by Arthur S. Reber& Emily S. Reber. After investigating the data, researcher found several findings. Researcher found three motivational Lexicons used by Anthony Robbins in his book Unlimited Power; think, challenge, and remember. Think used as a tool to lead his readers to come to their memory, re-identify some main points, and consider about the certain thing. Challenge used to pump readers’ emotion, gave a test, and invited them to take action. Remember used as a tool to bring back a piece of information he provided before and try to keep it in readers’ mind. Anthony’s motivational Lexicons in Unlimited Power also can use in the pedagogical field. Teacher can use them in the teaching-learning process as it determines the influential factor for learning process. Finally, researcher suggests that the further research about motivational Lexicon can be conducted in the different perspective and bring to other field. Then, enhancing knowledge and enriching references to make it more comprehensive.
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motivational Lexicon in anthony robbins unlimited power for pedagogical field psychological perspective
2015Co-Authors: Nur Faidatun NaimahAbstract:Motivators have great power in their Lexicon and beable to influence people to take action for achieving their excellent life. It is like Anthony Robbins with his book; Unlimited Power. In conducting the research, researcher formulated two problems; (1) how are motivational Lexicons in Unlimited Power analyzed from the psychological perspective, and (2) how can motivational Lexicons in Unlimited Power which are analyzed from psychological perspective take apart in the pedagogical field. This research used qualitativedescriptive and documentation as data collection technique. The data analysis technique that researcher used was content analysis since they were texts in Unlimited Power. In conducting this research, researcher used some tables to categorize chapter, page, data, and analysis, then analyzed by using psychological perspective and bringing the analysis into pedagogical field. The main tool for this research is psychological dictionary by Arthur S. Reber& Emily S. Reber. After investigating the data, researcher found several findings. Researcher found three motivational Lexicons used by Anthony Robbins in his book Unlimited Power; think, challenge, and remember. Think used as a tool to lead his readers to come to their memory, re-identify some main points, and consider about the certain thing. Challenge used to pump readers’ emotion, gave a test, and invited them to take action. Remember used as a tool to bring back a piece of information he provided before and try to keep it in readers’ mind. Anthony’s motivational Lexicons in Unlimited Power also can use in the pedagogical field. Teacher can use them in the teaching-learning process as it determines the influential factor for learning process. Finally, researcher suggests that the further research about motivational Lexicon can be conducted in the different perspective and bring to other field. Then, enhancing knowledge and enriching references to make it more comprehensive.
Mervat Gheith - One of the best experts on this subject based on the ideXlab platform.
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sentiment analysis for modern standard arabic and colloquial
arXiv: Computation and Language, 2015Co-Authors: Hossam Samir Ibrahim, Sherif M Abdou, Mervat GheithAbstract:The rise of social media such as blogs and social networks has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations, therefore many are now looking to the field of sentiment analysis. In this paper, we present a feature-based sentence level approach for Arabic sentiment analysis. Our approach is using Arabic idioms/saying phrases Lexicon as a key importance for improving the detection of the sentiment polarity in Arabic sentences as well as a number of novels and rich set of linguistically motivated features (contextual Intensifiers, contextual Shifter and negation handling), syntactic features for conflicting phrases which enhance the sentiment classification accuracy. Furthermore, we introduce an automatic expandable wide coverage polarity Lexicon of Arabic sentiment words. The Lexicon is built with gold-standard sentiment words as a seed which is manually collected and annotated and it expands and detects the sentiment orientation automatically of new sentiment words using synset aggregation technique and free online Arabic Lexicons and thesauruses. Our data focus on modern standard Arabic (MSA) and Egyptian dialectal Arabic tweets and microblogs (hotel reservation, product reviews, etc.). The experimental results using our resources and techniques with SVM classifier indicate high performance levels, with accuracies of over 95%.
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automatic expandable large scale sentiment Lexicon of modern standard arabic and colloquial
2015 First International Conference on Arabic Computational Linguistics (ACLing), 2015Co-Authors: Hossam Samir Ibrahim, Sherif M Abdou, Mervat GheithAbstract:In subjectivity and sentiment analysis (SSA), there are two main requirements are necessary to improve sentiment analysis effectively in any language and genres, first, high coverage sentiment Lexicon - where entries are tagged with semantic orientation (positive, negative and neutral) - second, tagged corpora to train the sentiment classifier. Much of research has been conducted in this area during the last decade, but the need of building these resources is still ongoing, especially for morphologically-Rich language (MRL) such as Arabic. In this paper, we present an automatic expandable wide coverage polarity Lexicon of Arabic sentiment words, this lexical resource explicitly devised for supporting Arabic sentiment classification and opinion mining applications. The Lexicon is built using a seed of gold-standard Arabic sentiment words which are manually collected and annotated with semantic orientation (positive or negative), and automatically expanded with sentiment orientation detection of the new sentiment words by exploiting some lexical information such as part-of-speech (POS) tags and using synset aggregation techniques from free online Arabic Lexicons, thesauruses. We report efforts to expand a manually-built our polarity Lexicon using different types of data. Finally, we used various tagged data to evaluate the coverage and quality of our polarity Lexicon, moreover, to evaluate the Lexicon expansion and its effects on the sentiment analysis accuracy. Our data focus on modern standard Arabic (MSA) and Egyptian dialectal Arabic tweets and Arabic microblogs (hotel reservation, product reviews, and TV program comments).
