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藉由公眾參與,打造全民期待的互動平台(二)
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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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La Russa “salva” la caserma Cernaia
C. Porchietto La Stampa Torino 30.05.09
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Claudia Porchietto
Dia Mund CriançA
Dia Mund CriançA
Tito Romeu Gomes de Sousa Maia Mendes
International Journal on Natural Language Computing (IJNLC)
HANDLING UNKNOWN WORDS IN NAMED ENTITY RECOGNITION USING TRANSLITERATION
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ijnlc
Gio Am1
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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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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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藉由公眾參與,打造全民期待的互動平台(五)
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