Speech Acts Classification of Persian Language Texts Using Three Machine Leaming Methods
The objective of this paper is to design a system to classify Persian speech acts. The driving vision for this work is to provide inteUigent systems such as text to speech, machine translation, text summarization, etc. that are sensitive to the speech acts of the input texts and can pronounce the co...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Iran Telecom Research Center
2010-03-01
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| Series: | International Journal of Information and Communication Technology Research |
| Subjects: | |
| Online Access: | http://ijict.itrc.ac.ir/article-1-272-en.html |
| _version_ | 1811169331109691392 |
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| author | Mohammad Mehdi Homayounpour Arezou Soltani Panah |
| author_facet | Mohammad Mehdi Homayounpour Arezou Soltani Panah |
| author_sort | Mohammad Mehdi Homayounpour |
| collection | DOAJ |
| description | The objective of this paper is to design a system to classify Persian speech acts. The driving vision for this work is to provide inteUigent systems such as text to speech, machine translation, text summarization, etc. that are sensitive to the speech acts of the input texts and can pronounce the corresponding intonation correctly. Seven speech acts were considered and 3 classification methods including (1) Naive Bayes, (2) K-Nearest Neighbors (KNN), and (3) Tree learner were used. The performance of speech act classification was evaluated using these methods including 10- Fold Cross-Validation, 70-30 Random Sampling and Area under ROC. KNN with an accuracy of 72% was shown to be the best classifier for the classification of Persian speech acts. It was observed that the amount of labeled training data had an important role in the classification performance. |
| first_indexed | 2024-04-10T16:41:43Z |
| format | Article |
| id | doaj.art-fbff77a875cc46d79b952dd67ac7ac46 |
| institution | Directory Open Access Journal |
| issn | 2251-6107 2783-4425 |
| language | English |
| last_indexed | 2024-04-10T16:41:43Z |
| publishDate | 2010-03-01 |
| publisher | Iran Telecom Research Center |
| record_format | Article |
| series | International Journal of Information and Communication Technology Research |
| spelling | doaj.art-fbff77a875cc46d79b952dd67ac7ac462023-02-08T07:29:32ZengIran Telecom Research CenterInternational Journal of Information and Communication Technology Research2251-61072783-44252010-03-01216571Speech Acts Classification of Persian Language Texts Using Three Machine Leaming MethodsMohammad Mehdi Homayounpour0Arezou Soltani Panah1 Lab. for Intelligent Signal and Speech Proc. Department of Computer Engineering and IT Amirkabir University of Technology Tehran, Iran Lab. for Intelligent Signal and Speech Proc. Department of Computer Engineering and IT Amirkabir University of Technology Tehran, Iran The objective of this paper is to design a system to classify Persian speech acts. The driving vision for this work is to provide inteUigent systems such as text to speech, machine translation, text summarization, etc. that are sensitive to the speech acts of the input texts and can pronounce the corresponding intonation correctly. Seven speech acts were considered and 3 classification methods including (1) Naive Bayes, (2) K-Nearest Neighbors (KNN), and (3) Tree learner were used. The performance of speech act classification was evaluated using these methods including 10- Fold Cross-Validation, 70-30 Random Sampling and Area under ROC. KNN with an accuracy of 72% was shown to be the best classifier for the classification of Persian speech acts. It was observed that the amount of labeled training data had an important role in the classification performance.http://ijict.itrc.ac.ir/article-1-272-en.htmlspeech actpersian languagetext processingtext to speechnaive bayesk-nearest neighborstree learner |
| spellingShingle | Mohammad Mehdi Homayounpour Arezou Soltani Panah Speech Acts Classification of Persian Language Texts Using Three Machine Leaming Methods International Journal of Information and Communication Technology Research speech act persian language text processing text to speech naive bayes k-nearest neighbors tree learner |
| title | Speech Acts Classification of Persian Language Texts Using Three Machine Leaming Methods |
| title_full | Speech Acts Classification of Persian Language Texts Using Three Machine Leaming Methods |
| title_fullStr | Speech Acts Classification of Persian Language Texts Using Three Machine Leaming Methods |
| title_full_unstemmed | Speech Acts Classification of Persian Language Texts Using Three Machine Leaming Methods |
| title_short | Speech Acts Classification of Persian Language Texts Using Three Machine Leaming Methods |
| title_sort | speech acts classification of persian language texts using three machine leaming methods |
| topic | speech act persian language text processing text to speech naive bayes k-nearest neighbors tree learner |
| url | http://ijict.itrc.ac.ir/article-1-272-en.html |
| work_keys_str_mv | AT mohammadmehdihomayounpour speechactsclassificationofpersianlanguagetextsusingthreemachineleamingmethods AT arezousoltanipanah speechactsclassificationofpersianlanguagetextsusingthreemachineleamingmethods |