Arabic Speech Act Recognition Using Bootstrapped Rule Based System

Lina Sherkawi(1*), Nada Ghneim(2), Oumayma Al Dakkak(3)

(1) Department of Artificial Intelligence, Information Technology Engineering Faculty, Damascus University, Syrian Arab Republic
(2) Higher Institute for Applied Sciences and Technology HIAST, Syrian Arab Republic
(3) Higher Institute for Applied Sciences and Technology HIAST, Syrian Arab Republic
(*) Corresponding author


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Abstract


This paper presents a rule-based approach to detect Arabic Speech Act types. Our approach is based on the fact that Arabic language is rich in morphology and that its syntax is inherently rule–based. The importance of Arabic Speech Act type recognition comes from its wide variety of application domains, such as: speech synthesis, speech and emotion recognition and conversational analysis. In this work, a rule-based Expert System has been developed in a bootstrapping manner, to classify an utterance speech act type to one of the sixteen types (interrogation, exclamation, negation, etc.). Our system was tested on a corpus of about 1500 sentences and achieved an accuracy rate of 98.92%. The expert system was used to automatically annotate a corpus (sentence-level annotation) by assigning a tag to each sentence according to its speech act type.
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Keywords


Bootstrapping; Corpus Annotation; Grammatical Classification; Sentence Type Recognition; Speech Act

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References


J. Austin, How to do things with words, MA: Harvard University Press, Cambridge, 1962.
http://dx.doi.org/10.1093/acprof:oso/9780198245537.001.0001

Moldovan, C., Rus, V., Graesser, A. C., Automated Speech Act Classification For Online Chat, Proceedings of the 22nd Midwest Artificial Intelligence and Cognitive Science Conference (Page: 23 Year of Publication: 2011).
http://dx.doi.org/10.1007/978-3-319-07221-0_28

M. Fišel, Machine learning techniques in dialogue act recognition, (2007) Estonian Papers in Applied Linguistics, 3, pp. 117 – 134.
http://dx.doi.org/10.5128/erya3.08

H. Bunt, Context and Dialogue Control, (1994) THINK Quarterly, 3 (1), pp. 19–31.
http://dx.doi.org/10.1075/nlp.1.03bun

J. Allen, M. Core, Draft of DAMSL: Dialog Act Markup in Several Layers, (1997) Technical Report,.pp. 1-32.
http://dx.doi.org/10.2172/5743726

Ezen-Can, A., Boyer, K.E., A Tutorial Dialogue System for Real-Time Evaluation of Unsupervised Dialogue Act Classifiers: Exploring System Outcomes, Proceedings of the international conference on artificial intelligence in education (AIED), Lecture Notes in Computer Science, vol. 9112. Springer, Cham, (Page: 105 Year of Publication: 2015).
http://dx.doi.org/10.1007/978-3-319-19773-9_11

Rus, V., Maharjan, N., Banjade, R., Dialogue Act Classification In Human-to-Human Tutorial Dialogues, Proceedings of Innovations in Smart Learning Conference, Lecture Notes in Educational Technology. Springer, Singapore, (Page: 183 Year of Publication: 2017).
http://dx.doi.org/10.1007/978-981-10-2419-1_25

Oraby, S., Gundecha, P., Mahmud, J., Bhuiyan, M., Akkiraju, R., "How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts, Proceedings of the 22nd International Conference on Intelligent User Interfaces (IUI '17), (Page: 343 Year of Publication: 2017).
http://dx.doi.org/10.1145/3025171.3025191

Elhawary, M., Elfeky, M., Mining Arabic Business Reviews, Proceedings of International Conference on Data Mining Workshops (ICDMW), IEEE, (Page: 1108 Year of Publication: 2010).
http://dx.doi.org/10.1109/icdmw.2010.24

Chaffar, S., Inkpen, D., Using a Heterogeneous Dataset for Emotion Analysis in Text, Proceedings of the 24th Canadian Conference on Artificial Intelligence (Canadian AI 2011), St. John’s, Canada, Springer, (Page: 62 Year of Publication: 2011).
http://dx.doi.org/10.1007/978-3-642-21043-3_8

H. Kim, C. N. Seon, J. Seo, Review of Korean Speech Act Classification: Machine Learning Methods, (2011) Journal of Computing Science and Engineering, 5 (4), pp. 288-293.
http://dx.doi.org/10.5626/jcse.2011.5.4.288

Zarisheva, E., Scheffler, T., Dialog Act Annotation for Twitter Conversations, Proceedings of the SIGDIAL 2015 Conference, (Page: 114 Year of Publication: 2015).
http://dx.doi.org/10.18653/v1/w15-4614

Khoo, A., Marom, Y., Albrecht, D., Experiments with Sentence Classification, Proceedings of the 2006 Australasian Language Technology Workshop (ALTW2006) (Page: 18 Year of Publication: 2006).
http://dx.doi.org/10.1109/slt.2006.326766

D Jurafsky, E Shriberg, and D Biasca. Switchboard SWBD-DAMSL Labeling Project Coder's Manual, Draft 13, (1997) Technical Report 97-02, University of Colorado Institute of Cognitive Science.
http://dx.doi.org/10.21236/ada607947

Keizer, S., op den Akker, R., Nijholt, A., Dialogue Act Recognition with Bayesian Networks for Dutch Dialogues, Third SIGdial Workshop on Discourse and Dialogue, (Page: 88 Year of Publication: 2002).
http://dx.doi.org/10.3115/1118121.1118134

Jurafsky, D., Shriberg, E., Fox, B., Curl, T., Lexical, Prosodic, and Syntactic Cues for Dialog Acts, Proceedings of ACL/COLING-98 Workshop on Discourse Relations and Discourse Markers, (Page: 114 Year of Publication: 1998).
http://dx.doi.org/10.3115/1667583.1667609

Zhang, R., Gao, D., Li, W., What Are Tweeters Doing: Recognizing Speech Acts in Twitter, Proceedings of the Workshop on Analyzing Microtext at the 25th AAAI Conference on Artificial Intelligence, (Page: 86 Year of Publication: 2011).
http://dx.doi.org/10.5220/0003179903290336

Rasor, T., Olney, A., D’Mello, S., Student Speech Act Classification Using Machine Learning, Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference, (Year of Publication: 2011).
http://dx.doi.org/10.1109/tai.1992.246431

Schabus, D., Krenn, B., Neubarth, F., Data-Driven Identification of Dialogue Acts in Chat Messages, Proceedings of the 13th Conference on Natural Language Processing (KONVENS), (Page: 236 Year of Publication: 2016).
http://dx.doi.org/10.1515/9783110821895-033

Shala, L., Rus, V., Graesser, A., Automatic Speech Act Classification In Arabic, Subjetividad y Procesos Cognitivos Conference, (Page: 284 Year of Publication: 2010).
http://dx.doi.org/10.1007/978-3-319-07221-0_28

Dbabis, S. B., Mallek, F., Ghorbel, H., Belguith, L., Dialogue Acts Annotation Scheme within Arabic discussions, Proceedings of SemDial 2012 The 16th workshop on semantics and pragmatics of dialogue Sorbonne, Paris, France, (Page: 88 Year of Publication: 2012).
http://dx.doi.org/10.1007/978-3-319-18111-0_35

Louwerse, M., Crossley, S., Dialog Act Classification Using N-Gram Algorithms, Proceedings of the Florida Artificial Intelligence Research Society International Conference (FLAIRS). Menlo Park, CA: AAAI Press, (Page: 758 Year of Publication: 2006).
http://dx.doi.org/10.1609/aimag.v31i3.2301

O’Shea, J., Bandar, Z., Crockett, K., A Machine Learning Approach to Speech Act Classification Using Function Words, Proceedings of the 4th KES International Symposium, KES-AMSTA 2010, Gdynia, Poland, Springer, Part II, (Page: 82 Year of Publication: 2010).
http://dx.doi.org/10.1007/978-3-642-13541-5_9

Marineau, J., Wiemer-Hastings, P., Harter, D., Olde, B., Chipman, P., Karnavat, A., Pomeroy, V., Rajan, S., Graesser, A., The Tutoring Research Group, Classification of Speech Acts in Tutorial Dialog, Proceedings of the Workshop on Modeling Human Teaching Tactics and Strategies at the Intelligent Tutoring Systems 2000 Conference (Page: 65 Year of Publication: 2000).
http://dx.doi.org/10.1007/3-540-68716-5_39

Y. Zhou, Q. Hu, J. Liu, Y. Jia, Combining heterogeneous deep neural networks with conditional random fields for Chinese dialogue act recognition, (2015) Neurocomputing, 168, pp. 408–417.
http://dx.doi.org/10.1016/j.neucom.2015.05.086

Choi, W. S., Cho, J. M., Seo, J., Analysis System of Speech Acts and Discourse Structures Using Maximum Entropy Model, Proceedings of the 37th annual meeting of the Association for Computational Linguistics, (Page: 230 Year of Publication: 1999).
http://dx.doi.org/10.3115/1034678.1034719

A. Elmadany, S. Abdou, M. Gheith, Towards Understanding Egyptian Arabic Dialogues, (2015) International Journal of Computer Applications, 120 (22), pp. 7-12.
http://dx.doi.org/10.5120/21390-4427

Dbabis, S.B., Ghorbel, H., Belguith, L.H., Kallel, M., Automatic Dialogue Act Annotation within Arabic Debates, Proceedings of International Conference on Intelligent Text Processing and Computational Linguistics (CICLing), Lecture Notes in Computer Science, vol 9041. Springer, Cham, (Page: 467 Year of Publication: 2015).
http://dx.doi.org/10.1007/978-3-319-18111-0_35

A. Ezen-Can, K. E. Boyer, Understanding Student Language: An Unsupervised Dialogue Act Classification Approach, (2015) Journal of Educational Data Mining, 7 (1), pp. 51–78.
http://dx.doi.org/10.1007/978-3-319-19773-9_11

Ezen-Can, A., Boyer, K. E., Combining Task and Dialogue Streams in Unsupervised Dialogue Act Models, Proceedings of the SIGDIAL 2014 Conference, (Page: 113 Year of Publication: 2014).
http://dx.doi.org/10.3115/v1/w14-4316

Tavafi, M., Mehdad, Y., Joty, S., Carenini, G., Ng, R., Dialogue Act Recognition in Synchronous and Asynchronous Conversations, Proceedings of the SIGDIAL 2013 Conference, (Page: 117 Year of Publication: 2013).
http://dx.doi.org/10.3115/v1/w14-3107

P. Král, C. Cerisara, Automatic dialogue act recognition with syntactic features, (2014) Language Resources and Evaluation, 48 (3), pp. 419–441.
http://dx.doi.org/10.1007/s10579-014-9263-6

Milajevs, D., Purver, M., Investigating the Contribution of Distributional Semantic Information for Dialogue Act Classification, Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC), (Page: 40 Year of Publication: 2014).
http://dx.doi.org/10.3115/v1/w14-1505

Sadohara, K., Kojima, H., Narita, T., Nihei, M., Kamata, M., Onaka, S., Fujita, Y., Inoue, T., Sub-lexical Dialogue Act Classification in a Spoken Dialogue System Support for the Elderly with Cognitive Disabilities, Proceedings of 4th Workshop on Speech and Language Processing for Assistive Technologies (SLPAT), (Page: 93 Year of Publication: 2013).
http://dx.doi.org/10.21437/slpat.2016-2

F. H. Al-Hindawi, H. H. Al-Masu’di, R. F. Mirza, The Speech Act Theory in English and Arabic, (2014) Open Journal of Modern Linguistics, 4 (1), pp. 27-37.
http://dx.doi.org/10.4236/ojml.2014.41003

M. Boudchiche, A. Mazroui, M. A. Bebah, A. Lakhouaja, A. Boudlal, AlKhalil Morpho Sys 2: A robust Arabic morpho-syntactic analyzer, (2017) Journal of King Saud University – Computer and Information Sciences, 29 (2), pp. 141-146. http://sourceforge.net/projects/alkhalil/.
http://dx.doi.org/10.1016/j.jksuci.2016.05.002

Abdennadher, S., Aly, A., B¨uhler, D., Minker, W., Pittermann, J., BECAM Tool - A Semi-automatic Tool for Bootstrapping Emotion Corpus Annotation and Management, European Conference on Speech and Language Processing (EUROSPEECH), Antwerp, Belgium, (Page: 946 Year of Publication: 2007).
http://dx.doi.org/10.1049/cp:20070377

M. Sokolova, G. Lapalme, A systematic analysis of performance measures for classification tasks, (2009) Information Processing and Management Journal, 45 (4), pp. 427–437.
http://dx.doi.org/10.1016/j.ipm.2009.03.002


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