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藉由公眾參與，打造全民期待的互動平台(一）
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Maintaing cash is the best 'put' option one can have..said Jagannadham Thunuguntla.
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In this work we present our expert system of Automatic reading or speech synthesis based on a text written in Standard Arabic, our work is carried out in two great stages: the creation of the sound data base, and the transformation of the written text into speech (Text To Speech TTS). This transformation is done firstly by a Phonetic Orthographical Transcription (POT) of any written Standard Arabic text with the aim of transforming it into his corresponding phonetics sequence, and secondly by the generation of the voice signal which corresponds to the chain transcribed. We spread out the different of conception of the system, as well as the results obtained compared to others works studied to realize TTS based on Standard Arabic.
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Moj Deo Svemira
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Spoken language understanding (SLU) is a key requirement of spoken dialogue systems (SDS). The role of SLU parser is to robustly interpret the meanings of users’ utterance using a hand-crafted grammar that is expensive to build. This task becomes even harder when the developer is creating a SLU grammar for inflectional languages due to the different conjugations and declensions. This causes long grammar definition files that are hard to structure and also to manage. In this paper, we propose a new and alternative method, called Smart Grammar to facilitate the development of speech enabled applications. This uses a morphological analyzer, in addition to the semantic parser, in order to convert each user utterance in the canonical form.
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This paper introduces a Vietnamese automatic dialog mechanism which allows the V-DLG~TABL system to automatically communicate with clients. This dialog mechanism is based on the Question – Answering engine of V-DLG~TABL system, and composes the following supplementary mechanisms: 1) the mechanism of choosing the suggested question; 2) the mechanism of managing the conversations and suggesting information; 3) the mechanism of resolving the questions having anaphora; 4) the scenarios of dialog (in this stage, there is only one simple scenario defined for the system).
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藉由公眾參與，打造全民期待的互動平台(四）
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Part of speech tagging (POS tagging) has a crucial role in different fields of natural language processing (NLP) including Speech Recognition, Natural Language Parsing, Information Retrieval and Multi Words Term Extraction. This paper proposes an efficient and accurate POS Tagging technique for Arabic language using hybrid approach. Due to the ambiguity issue, Arabic Rule-Based method suffers from misclassified and unanalyzed words. To overcome these two problems, we propose a Hidden Markov Model (HMM) integrated with Arabic Rule-Based method. Our POS tagger generates a set of three POS tags: Noun, Verb, and Particle. The proposed technique uses the different contextual information of the words with a variety of the features which are helpful to predict the various POS classes. To evaluate its accuracy, the proposed method has been trained and tested with two corpora: the Holy Quran Corpus and Kalimat Corpus for undiacritized Classical Arabic language. The experiment results demonstrate the efficiency of our method for Arabic POS Tagging. In fact, the obtained accuracies rates are 97.6%, 96.8% and 94.4% for respectively our Hybrid Tagger, HMM Tagger and for the Rule-Based Tagger with Holy Quran Corpus. And for Kalimat Corpus we obtained 94.60%, 97.40% and 98% for respectively Rule-Based Tagger, HMM Tagger and our Hybrid Tagger.
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藉由公眾參與，打造全民期待的互動平台(五）
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The process of browsing Search Results is one of the major problems with traditional Web search engines for English, European, and any other languages generally, and for Arabic Language particularly. This process is absolutely time consuming and the browsing style seems to be unattractive. Organizing Web search results into clusters facilitates users quick browsing through search results. Traditional clustering techniques (data-centric clustering algorithms) are inadequate since they don't generate clusters with highly readable names or cluster labels. To solve this problem, Description-centric algorithms such as Suffix Tree Clustering (STC) algorithm have been introduced and used successfully and extensively with different adapted versions for English, European, and Chinese Languages. However, till the day of writing this paper, in our knowledge, STC algorithm has been never applied for Arabic Web Snippets Search Results Clustering.
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Spoken language understanding (SLU) is a key requirement of spoken dialogue systems (SDS). The role of SLU parser is to robustly interpret the meanings of users’ utterance using a hand-crafted grammar that is expensive to build. This task becomes even harder when the developer is creating a SLU grammar for inflectional languages due to the different conjugations and declensions. This causes long grammar definition files that are hard to structure and also to manage. In this paper, we propose a new and alternative method, called Smart Grammar to facilitate the development of speech enabled applications. This uses a morphological analyzer, in addition to the semantic parser, in order to convert each user utterance in the canonical form.
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Building a vietnamese dialog mechanism for v dlg~tabl system
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ijnlc
International Journal on Natural Language Computing (IJNLC)
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藉由公眾參與，打造全民期待的互動平台(四）
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Un buen emprendedor se "lanza a la piscina" sin miedo.
3ªHistoria: Lánzate a la Piscina
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Part of speech tagging (POS tagging) has a crucial role in different fields of natural language processing (NLP) including Speech Recognition, Natural Language Parsing, Information Retrieval and Multi Words Term Extraction. This paper proposes an efficient and accurate POS Tagging technique for Arabic language using hybrid approach. Due to the ambiguity issue, Arabic Rule-Based method suffers from misclassified and unanalyzed words. To overcome these two problems, we propose a Hidden Markov Model (HMM) integrated with Arabic Rule-Based method. Our POS tagger generates a set of three POS tags: Noun, Verb, and Particle. The proposed technique uses the different contextual information of the words with a variety of the features which are helpful to predict the various POS classes. To evaluate its accuracy, the proposed method has been trained and tested with two corpora: the Holy Quran Corpus and Kalimat Corpus for undiacritized Classical Arabic language. The experiment results demonstrate the efficiency of our method for Arabic POS Tagging. In fact, the obtained accuracies rates are 97.6%, 96.8% and 94.4% for respectively our Hybrid Tagger, HMM Tagger and for the Rule-Based Tagger with Holy Quran Corpus. And for Kalimat Corpus we obtained 94.60%, 97.40% and 98% for respectively Rule-Based Tagger, HMM Tagger and our Hybrid Tagger.
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藉由公眾參與，打造全民期待的互動平台(五）
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frontiertw
The process of browsing Search Results is one of the major problems with traditional Web search engines for English, European, and any other languages generally, and for Arabic Language particularly. This process is absolutely time consuming and the browsing style seems to be unattractive. Organizing Web search results into clusters facilitates users quick browsing through search results. Traditional clustering techniques (data-centric clustering algorithms) are inadequate since they don't generate clusters with highly readable names or cluster labels. To solve this problem, Description-centric algorithms such as Suffix Tree Clustering (STC) algorithm have been introduced and used successfully and extensively with different adapted versions for English, European, and Chinese Languages. However, till the day of writing this paper, in our knowledge, STC algorithm has been never applied for Arabic Web Snippets Search Results Clustering.
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藉由公眾參與，打造全民期待的互動平台(五）
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CLUSTERING WEB SEARCH RESULTS FOR EFFECTIVE ARABIC LANGUAGE BROWSING
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