MOST CITED NATURAL LANGUAGECOMPUTING ARTICLESIN 2017

MOST CITED NATURAL LANGUAGE COMPUTING ARTICLES IN 2017

International Journal on Natural Language Computing (IJNLC) ISSN: 2278 - 1307 [Online]; 2319 - 4111 [Print].

http://airccse.org/journal/ijnlc/index.html

GRAMMAR CHECKERS FOR NATURAL LANGUAGES: A RE VI E W Nivedita S. Bhirud1 , R.P. Bhavsar2 1

Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, India 2,3School of Computer Sciences, North Maharashtra University, Jalgaon, India

ABSTRACT Natural Language processing is an interdisciplinary branch of linguistic and computer science studied under the Artificial Intelligence (AI) that gave birth to an allied area called ‘Computational Linguistics’ which focuses on processing of natural languages on computational devices. A natural language consists of many sentences which are meaningful linguistic units involving one or more words linked together in accordance with a set of predefined rules called ‘grammar’. Grammar checking is fundamental task in the formal world that validates sentences syntactically as well as semantically. Grammar Checker tool is a prominent tool within language engineering. Our review draws on the till date development of various Natural Language grammar checkers to look at past, present and the future in the present context. Our review covers common grammatical errors , overview of grammar checking process, grammar checkers of various languages with the aim of seeking their approaches, methodologies and performance evaluation, which would be great help for developing new tool and system as a whole. The survey concludes with the discussion of different features included in existing grammar checkers of foreign languages as well as a few Indian Languages.

KEYWORDS Natural Language Processing, Computational Linguistics, Writing errors,Grammatical mistakes, Grammar Checker

For More Details: http://aircconline.com/ijnlc/V6N4/6417ijnlc01.pdf Volume Link : http://airccse.org/journal/ijnlc/vol6.html

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[6] Bal Krishna Bal, Prajol Shrestha, “Architectural and System Design of the Nepali Grammar Checker”, www.panl10n.net/english/.../Nepal/Microsoft%20Word%20-%208_OK_N_400.pdf [7] Kinoshita, Jorge; Nascimento, Laнs do; Dantas ,Carlos Eduardo. ”CoGrOO: a BrazilianPortuguese Grammar Checker based on the CETENFOLHA Corpus”. Universidade da Sгo Paulo (USP), Escola Politйcnica. 2003.

[8] Domeij, Rickard; Knutsson, Ola; Carlberger, Johan; Kann, Viggo. “Granska: An efficient hybrid system for Swedish grammar checking”. Proceedings of the 12th Nordic conference in computational linguistic, Nodalida- 99. 2000.

[9] Jahangir Md; Uzzaman, Naushad; Khan, Mumit. “N-Gram Based Statistical Grammar Checker For Bangla And English”. Center for Research On Bangla Language Processing.

Bangladesh, 2006.

[10] Singh, Mandeep; Singh, Gurpreet; Sharma, Shiv. “A Punjabi Grammar Checker”. Punjabi University. 2nd international conference of computational linguistics: Demonstration paper. 2008. pp. 149 – 132.

[11] Steve Richardson. “Microsoft Natuaral language Understanding System and Grammar checker”. Microsoft.USA, 1997.

[12] Daniel Naber. “A Rule-Based Style And Grammar Checker”. Diplomarbeit. Technische Fakultät Bielefeld, 2003 [13] “Brief History of Grammar Check Software”, available at: http://www.grammarcheck.net/briefhistory-of-grammar-check software/ , Accessed On October 28, 2011 International Journal on Natural Language Computing (IJNLC) Vol. 6, No.4, August 2017 13

[14] Gelbukh, Alexander. “Special issue: Natural Language Processing and its Applications”.

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[15] H. Kabir, S. Nayyer, J. Zaman, and S. Hussain, “Two Pass Parsing Implementation for an Urdu Grammar Checker.”

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[17] LataBopche, GauriDhopavkar, and ManaliKshirsagar, “Grammar Checking System Using Rule Based Morphological Process for an Indian Language”, Global Trends in Information Systems and Software Applications, 4th International Conference, ObCom 2011 Vellore, TN, India, December 9-11, 2011.

[18] MadhaviVaralwar, Nixon Patel. “Characteristics of Indian Languages” available at http://http://www.w3.org/2006/10/SSML/papers/CHARACTERISTICS_OF_INDIAN_LANGU AG ES.pdf on 30/12/2013 [19] Kenneth W. Church andLisa F. Rau, “CommercialApplications of Natural Language Processing”, COMMUNICATIONS OF THE ACM ,Vol. 38, No. 11, November 1995 [20] ChandhanaSurabhi.M, “Natural Language Processing Future”, Proceedings of International Conference on Optical Imaging Sensor and Security, Coimbatore, Tamil Nadu, India, July 2-3, 2013 [21] ER-QING XU , “ NATURAL LANGUAGE GENERATION OF NEGATIVE SENTENCES IN THE MINIMALIST PARADIGM”, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 18-21 August 2005 [22] Jurafsky Daniel, H., James. Speech and Language Processing: “An introduction to natural language processing, computational linguistics and speech recognition” June 25, 2007.

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[24] S., Philip; M.W., David. “A Statistical Grammar Checker”. Department of Computer Science. Flinders University of South Australia.South Australia, 1996 [25] Mochamad Vicky Ghani Aziz, Ary Setijadi Prihatmanto, Diotra Henriyan, Rifki W�jaya, “Design and Implementation of Natural Language Processing with Syntax and Semantic Analysis for Extract Traffic Conditions from Social Media Data” , IEEE 5th International Conference on System Engineering and Technology,Aug.10-11,UiTM,ShahAlam,Malaysia,2015

USING OBJECTIVE WORDS IN THE REVIEWS TO IMPROVE THE COLLOQUIAL ARABIC SENTIMENT ANALYSIS Omar Al-Harbi Department of Computer and Information, Community College, Jazan University, Jazan, KSA

ABSTRACT One of the main difficulties in sentiment analysis of the Arabic language is the presence of the colloquialism. In this paper, we examine the effect of using objective words in conjunction with sentimental words on sentiment classification for the colloquial Arabic reviews, specifically Jordanian colloquial reviews. The reviews often include both sentimental and objective words; however, the most existing sentiment analysis models ignore the objective words as they are considered useless. In this work, we created two lexicons: the first includes the colloquial sentimental words and compound phrases, while the other contains the objective words associated with values of sentiment tendency based on a particular estimation method. We used these lexicons to extract sentiment features that would be training input to the Support Vector Machines (SVM) to classify the sentiment polarity of the reviews. The reviews dataset have been collected manually from JEERAN website. The results of the experiments show that the proposed approach improves the polarity classification in comparison to two baseline models, with accuracy 95.6%.

KEYWORDS Arabic sentiment analysis, opinion mining, colloquial Arabic language, colloquial Jordanian reviews For More Details : http://aircconline.com/ijnlc/V6N3/6317ijnlc01.pdf Volume Link : http://airccse.org/journal/ijnlc/vol6.html

REFERENCES [1] H. Tang, S. Tan, and X. Cheng, “A survey on sentiment detection of reviews,” Expert Systems with Applications, vol. 36, no. 7, pp. 10 760–10 773, 2009.

[2] M. Hu and B. Liu, “Mining and summarizing customer reviews,” in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2004, pp. 168– 177.

[3] B. Liu, “Sentiment analysis and subjectivity.” Handbook of natural language processing, vol.

2, pp. 627–666, 2010.

[4] W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, vol. 5, no. 4, pp. 1093–1113, 2014.

[5] B. Liu, “Sentiment analysis and opinion mining,” Synthesis lectures on human language technologies,vol. 5, no. 1, pp. 1–167, 2012.

[6] M. Taboada, J. Brooke, M. To_loski, K. Voll, and M. Stede, “Lexicon-based methods for sentiment analysis,” Computational linguistics, vol. 37, no. 2, pp. 267–307, 2011.

[7] A. Kennedy and D. Inkpen, “Sentiment classi_cation of movie reviews using contextual valence shifters,” Computational intelligence, vol. 22, no. 2, pp. 110–125, 2006.

[8] M. Yang, W. Tu, Z. Lu, W. Yin, and K.-P. Chow, “Lcct: a semisupervised model for sentiment classi_- cation,” in Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL. Association for Computational Linguistics (ACL)., 2015.

[9] K. Shaalan, H. Bakr, and I. Ziedan, “Transferring egyptian colloquial dialect into modern standard arabic,” in International Conference on Recent Advances in Natural Language Processing (RANLP–2007), Borovets, Bulgaria, 2007, pp. 525–529.

[10] M. Abdul-Mageed, S. Kuebler, and M. Diab, “Samar: A system for subjectivity and sentiment analysis of social media arabic,” in Proceedings of the 3rdWorkshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA), ICC Jeju, Republic of Korea, 2012.

[11] R. Duwairi, R. Marji, N. Sha’ban, and S. Rushaidat, “Sentiment analysis in arabic tweets,” in Information and communication systems (icics), 2014 5th international conference on. IEEE, 2014, pp. 1–6.

[12] H. ElSahar and S. R. El-Beltagy, “A fully automated approach for arabic slang lexicon extraction from microblogs,” in International Conference on Intelligent Text Processing and Computational Linguistics. Springer, 2014, pp. 79–91.

[13] B. Pang and L. Lee, “A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts,” in Proceedings of the 42nd annual meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2004, p. 271.

[14] N. A. Abdulla, N. A. Ahmed, M. A. Shehab, and M. Al-Ayyoub, “Arabic sentiment analysis: Lexiconbased and corpus-based,” in Applied Electrical Engineering and Computing Technologies (AEECT), 2013 IEEE Jordan Conference on. IEEE, 2013, pp. 1–6.

[15] R. M. Duwairi, “Sentiment analysis for dialectical arabic,” in Information and Communication Systems (ICICS), 2015 6th International Conference on. IEEE, 2015, pp.

166–170.

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[19] P. McCarthy and C. Boonthum-Denecke, Applied natural language processing. Information Science Reference, 2011.

[20] M. A. M. E. Ahmed, “Alarge-scale computational processor of the arabic morphology, and applications,” Ph.D. dissertation, Faculty of Engineering, Cairo University Giza, Egypt, 2000.

[21] N. Y. Habash, “Introduction to arabic natural language processing,” Synthesis Lectures on Human Language Technologies, vol. 3, no. 1, pp. 1–187, 2010.

[22] R. Hetzron, The Semitic Languages. Routledge, 2013.

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[24] N. Habash and O. Rambow, “Magead: a morphological analyzer and generator for the arabic dialects,” in Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics. Association for Computational inguistics, 2006, pp. 681–688.

[25] C. Miller, E. Al-Wer, D. Caubet, J. C. Watson et al., Arabic in the city: Issues in dialect contact and language variation. Routledge, 2007.

BUILDING A SYLLABLE DATABASE TO SOLVE THE PROBLEM OF KHMER WORD SEGMENTATION Tran Van Nam1 , Nguyen Thi Hue2 and Phan Huy Khanh3 1

Department of Computer Engineering; University of Tra Vinh, Vietnam 2 School of Southern Khmer Language; University of Tra Vinh, Vietnam 3Department of Computer Engineering; Polytechnic University of Da Nang, Vietnam

ABSTRACT Word segmentation is a basic problem in natural language processing. With the languages having the complex writing system like the Khmer language in Southern of Vietnam, this problem really very intractable, posing the significant challenges. Although there are some experts in Vietnam as well as international having deeply researched this problem, there are still no reasonable results meeting the demand, in particular, no treated thoroughly the ambiguous phenomenon, in the process of Khmer language processing so far. This paper present a solution based on the syllable division into component clusters using two syllable models proposed, thereby building a Khmer syllable database, is still not actually available. This method using a lexical database updated from the online Khmer dictionaries and some supported dictionaries serving role of training data and complementary linguistic characteristics. Each component cluster is labelled and located by the first and last letter to identify entirety a syllable. This approach is workable and the test results achieve high accuracy, eliminate the ambiguity, contribute to solving the problem of word segmentation and applying efficiency in Khmer language processing.

KEYWORDS Ambiguity; component cluster; labelling; lexical database; natural language processing; syllable database; syllable formation; word segmentation.

For More Details: http://aircconline.com/ijnlc/V6N1/6117ijnlc01.pdf Volume Link: http://airccse.org/journal/ijnlc/vol6.html

REFERENCES [1] Hắc Sóc Hi, (2012) Ngữ pháp tiếng Khmer, Học viện Giáo dục Dân tộc.

[2] Hồ Xuân Mai, (2012) Vài nét về tiếng Khmer Nam Bộ (trường hợp ở tỉnh Sóc Trăng và Trà Vinh).Tạp chí Khoa học Xã hội, Số 12(172).

[3] K. Sok, (2004) Khmer Language Grammar, First Edition of Royal Academic of Cambodia.

[4] Ly Vattana. (2011) Các tiếp cận tách từ tiếng Khmer dùng trong cơ sở dữ liệu văn bản. Tạp chí Khoa học ĐHQGHN, Khoa học Tự nhiên và Công nghệ 27 pp251-258.

[5] Mai Ngọc Chừ, Vũ Đức Nghiệu, Hoàng Trọng Phiến. (1997) Cơ sở ngôn ngữ học và tiếng Việt. NXB Giáo dục.

[6] Narin Bi, Nguonly Taing. (2014) Khmer word segmentation based on bi-directional maximal matching for plaintext and microsoft word document. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014, Chiang Mai, Thailand, IEEE, December 9-12, pages 1–9.

[7] Sopheap Seng, Sethserey Sam, Viet-Bac Le, Brigitte Bigi, Laurent Besacier. (2008) Which units for acoustic and language modeling for Khmer automatic speech recognition.

International Workshop on Spoken Languages Technologies for Under-Ressourced Languages (SLTU'08).

[8] Villavon Souksan, Phan Huy Khánh. (2013) Tách từ tiếng Lào sử dụng kho ngữ vựng kết hợp với các đặc trưng ngữ pháp tiếng Lào. Hội thảo quốc gia lần thứ XVI, 14-15/11/2013.

[9] Villavon Souksan, Phan Huy Khánh. (2013) Khử bỏ nhập nhằng trong bài toán tách từ tiếng Lào. Tạp chí Khoa học & Công nghệ ĐH Đà Nẵng no1 (62), pp 113-119.

[10] Vichet Chea, Ye Kyaw Thu, Chenchen Ding, Masao Utiyama, Andrew Finch, Eiichiro Sumita. Khmer Word (2015) Segmentation Using Conditional Random Fields. In Khmer Natural Language Processing, December 4.

[11] Chea Sok Huor, Top Rithy, Ros Pich Hemy, Vann Navy. Word Bigram Vs Orthographic Syllable Bigram in Khmer Word Segmentation. pp 249-253 [12] C. Chan P. Wong (1996). Chinese Word Segmentation Based on Maximum Matching and Word Binding Force. Proceedings of Coling 96, p200-203 [13] Paisarn Charoenpornsawat (1998), Các phương pháp tách từ tiếng Thái, trường Đại học Chulalongkorn, Thái Land.

[14] Dinh Dien, Hoang Kiem, Nguyen Van Toan (2001), Vietnamese Word Segmentation, pp.

749 -756. The 6th Natural Language Processing Pacific Rim Symposium, Tokyo, Japan.

[15] Dien Dinh (2005), “Building an Annotated English-Vietnamese parallel Corpus”, MKS: A Journal of Southeast Asian Linguistics and Languages, Vol.35, pp.21-36.

[16] Đinh Điền (3/2005), “Xây dựng và khai thác ngữ liệu song ngữ Anh-Việt điện tử“, luận án tiến sĩ ngôn ngữ học so sánh, ĐH Khoa học Xã hội & Nhân văn, ĐHQG Tp.HCM. [17] Khưn Sóc (2007), Ngữ pháp tiếng Khmer, NXB Viện Phật học Campuchia.

[18] Viện nghiên cứu và đào tạo phía Nam (1998), Ngữ pháp tiếng Khmer, NXB Văn hóa dân tộc.

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[20] Long Siêm (2010), Vấn đề từ vưng tiếng Khmer, NXB Viện ngôn ngữ học Cam Puchia; [21] Chan Som Nop (2010), Từ và Các phương thức cấu tạo từ trong tiếng Khmer, NXB Campuchia.

[22] Bộ gõ Khmer Unicode. Web: http://dongthapit.com/tieng-khmer-campuchia/bo-go-khmerunicodecampuchia/ [23]

Từ điển trực tuyến Việt-Khmer.

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[24] Dạy học tiếng Khmer. Web: http://mmhomepage.com/burmese/Easy-Khmer-TiengCampuchia/

PATENT DOCUMENT SUMMARIZATION USING CONCEPTUAL GRAPHS Pattabhi R K Rao and Sobha Lalitha Devi AU-KBC Research Centre, MIT Campus Anna University, Chromepet, Chennai, India ABSTRACT In this paper a methodology to mine the concepts from documents and use these concepts to generate an objective summary of the claims section of the patent documents is proposed.

Conceptual Graph (CG) formalism as proposed by Sowa (Sowa 1984) is used in this work for representing the concepts and their relationships. Automatic identification of concepts and conceptual relations from text documents is a challenging task. In this work the focus is on the analysis of the patent documents, mainly on the claim’s section (Claim) of the documents. There are several complexities in the writing style of these documents as they are technical as well as legal. It is observed that the general in-depth parsers available in the open domain fail to parse the ‘claims section’ sentences in patent documents. The failure of in-depth parsers has motivated us, to develop methodology to extract CGs using other resources. Thus in the present work shallow parsing, NER and machine learning technique for extracting concepts and conceptual relationships from sentences in the claim section of patent documents is used. Thus, this paper discusses i) Generation of CG, a semantic network and ii) Generation of abstractive summary of the claims section of the patent. The aim is to generate a summary which is 30% of the whole claim section. Here we use Restricted Boltzmann Machines (RBMs), a deep learning technique for automatically extracting CGs. We have tested our methodology using a corpus of 5000 patent documents from electronics domain. The results obtained are encouraging and is comparable with the state of the art systems.

KEYWORDS Concept Mining, Document Summarization, Abstractive Summary, Conceptual Graphs, Machine Learning, Restricted Boltzmann Machines, Patent Documents

For More Details : http://aircconline.com/ijnlc/V6N3/6317ijnlc02.pdf Volume Link : http://airccse.org/journal/ijnlc/vol6.html

REFERENCES [1] Amati, G. and Ounis, I., (2000) “Conceptual Graphs and First Order Logic”, The Computer Journal, 43(1):1-12.

[2] Athanasios, K., Margarita, K., and Marinos K., (2004) “Geographic Knowledge Representation Using Conceptual Graphs”, In the Proceedings of 7th AGILE Conference on Geographic Information Science, Greece.

[3] Blum, N., (2001) “A Simplified Realization of the Hopcroft-Karp Approach to Maximum Matching in General Graphs”, Tech. Rep. 85232-CS, Computer Science Department, Univ. of Bonn.

[4] Bezdek J.C., (1981) “Pattern Recognition with Fuzzy Objective Function Algorithms”, Plenum Press, New York.

[5] Brin and Page, L., (1998) “The anatomy of a large scale hypertextual Web search engine”, Computer Networks and ISDN Systems, Vol 30, pp. 1-7.

[6] Brill, Eric., (1994), “Some Advances in transformation Based Part of Speech Tagging”. In Proceedings of the Twelfth International Conference on Artificial Intelligence (AAAI-94), Seattle, WA. [7] Deigo, M., Menno V. and Zaanen., (2005) “Learning of Graph Rules for Question Answering”, In the Proceedings of the Australasian Language Technology, Workshop 2005, Sydney, Australia. 15–23.

[8] Erkan and Radev, D., (2004) “Lexpagerank: Prestige in multi document text summarization”, In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Barcelona, Spain.

[9] Hinton, G. and Salakhutdinov, R., (2006) “Reducing the dimensionality of data with neural networks”, Science, 313(5786):504 – 507.

[10] George Giannakopoulos, Jeff Kubina, John M Conroy, Josef Steinberger, Benoit Favre, Mijail Kabadjov, Udo Kruschwitz, Massimo Poesio., (2015), “MultiLing 2015: Multilingual Summarization of Single and Multi-Documents, On-line Fora, and Call-center Conversations”, In: Proceedings of SIGDIAL, Prague pp. 270-274.

[11] Hopcroft, J.E., Karp, R.M., (1973) “An n5/2 algorithm for maximum matchings in bipartite graphs”, SIAM Journal on Computing, Vol 2(4), pp. 225-231, doi:10.1137/0202019.

[12] John F. S., (1976) “Conceptual Graphs for a Data Base Interface”, IBM Journal of Research and Development 20(4). 336–357.

[13] John F.S., (1984) “Connceptual Structures – Information Processing in Mind and Machine”.

Addison Wesley.

[14] Lin and Hovy, E.H., (2002) “From Single to Multi document Summarization: A Proto-type System and its Evaluation”, In Proceedings of ACL-2002.

[15] Lin, C.Y., (2004) “ROUGE: A package for automatic evaluation of summaries”, In Proceedings of the Workshop on Text Summarization Branches Out, Barcelona, Spain.

[16] Lin, C.Y and Hovy, E., (2003) “Automatic evaluation of summaries using n-gram cooccurrence”, In Proceedings of 2003 Language Technology Conference (HLT-NAACL 2003), Edmonton, Canada.

[17] Luhn, H.P., (1958) “The automatic creation of literature abstracts”, IBM Journal of Research and development, Vol 2(2), pp. 159-165.

[18] Inderjeet Mani., (2001) “Summarization Evaluation: An Overview”, In Proceedings of NTCIR. [19] Malarkodi C.S and Sobha Lalitha Devi, (2012) “A Deeper Look into Features for NE Resolution in Indian Languages”, In Proceedings of Workshop on Indian Language Data: Resources and Evaluation, LREC 2012, Istanbul.

[20] McKeownand, K. and Radev, D., (1995) “Generating summaries of multiple news articles”, In Proceedings of the 18th Annual International ACM, Seattle, WA, pp.74-82.

[21] Mihalcea R., (2004) “Graph-based ranking algorithms for sentence extraction, applied to text summarization”, In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004), Barcelona, Spain.

[22] Mihalcea and Tarau.P., (2004) “TextRank - bringing order into texts”, In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2004), Barcelona, Spain.

[23] Mihalcea, P.T., and Figa, E., (2004) “PageRank on semantic networks, with applica-tion to word sense disambiguation”, In Proceedings of the 20th International Con-ference on Computational Linguistics (COLING 2004), Geneva, Switzerland.

[24] Lafferty, J., McCallum, A., and Pereira, F., (2001) “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data”, In Proceedings of the 18th International Conference on Machine Learning (ICML-2001), pp.282-289.

[25] Menaka S., Pattabhi R.,K.,R. and Sobha, L.D., (2011) “Automatic Identification of CauseEffect Relations in Tamil Using CRFs”, In A. Gelbukh (eds), Springer LNCS volume 6608/2011, 316-327.

EMPLOYING PIVOT LANGUAGE TECHNIQUE THROUGH STATISTICAL AND NEURAL MACHINE TRANSLATION FRAMEWORKS: THE CASE OF UNDER-RESOURCED PERSIAN-SPANISH LANGUAGE PAIR Benyamin Ahmadnia and Javier Serrano Autonomous University of Barcelona, Cerdanyola del Valles, Spain

ABSTRACT The quality of Neural Machine Translation (NMT) systems like Statistical Machine Translation (SMT) systems, heavily depends on the size of training data set, while for some pairs of languages, high-quality parallel data are poor resources. In order to respond to this low-resourced training data bottleneck reality, we employ the pivoting approach in both neural MT and statistical MT frameworks. During our experiments on the Persian-Spanish, taken as an underresourced translation task, we discovered that, the aforementioned method, in both frameworks, significantly improves the translation quality in comparison to the standard direct translation approach.

KEYWORDS Statistical Machine Translation, Neural Machine Translation, Pivot Language Technique

For More Details : http://aircconline.com/ijnlc/V6N5/6517ijnlc03.pdf Volume Link : http://airccse.org/journal/ijnlc/vol6.html

REFERENCES [1] Al-Hunaity, Mossab & Maegaard, Bente & Hansen, Dorte, (2010) “Using English as a Pivot Language to Enhance Danish-Arabic Statistical Machine Translation”.

[2] Babych, Bogdan & Hartley, Anthony & Sharoff, Serge & Mudraya, Olga, (2007) “Assisting Translators in Indirect LexicalTransfer”, ACL. [3] Bahdanau, Dzmitry & Cho, Kyunghyun & Bengio, Yoshua, (2015) “Neural machine translation by jointly learning to align and translate”, ICLR. [4] Berger, Adam.L & Della Pietra, Stephen & Della Pietra, Vincent.J, (1996) “A Maximum Entropy Approach to Natural Language Processing”, Journal of Computational Linguistics.

[5] Bertoldi, Nicola & Barbaiani, Madalina & Federico, Marcello Cattoni, Roldano, (2008) “Phrase-based statistical machine translationwith pivot languages”, IWSLT. [6] Cheng, Y.ong & Liu, Yang & Yang, Qian & Sun, Maosong & Xu, Wei, (2016) “Neural Machine Translation with Pivot Languages”, ACL. [7] Cheng, Yong & Xu, Wei & He, Zhongjun & He, Wei & Wu, Hua & Sun, Maosong & Liu, Yang, (2016) “Semi-Supervised Learning for Neural Machine Translation”, ACL. [8] Cho, Kyunghyun & Merrienboer, Bert.V & Gulccehre, Caglar & Bougares, Fethi & Schwenk, Holger & Bengio, Yoshua, (2014) “Learning Phrase Representations using RNN EncoderDecoder for Statistical Machine Translation”.

[9] Cohn, Trevor & Hoang, Cong.D.V & Vymolova, Ekaterina & Yao, Kaisheng & Dyer, Chris & Haffari, Gholamreza, (2016) “Incorporating Structural Alignment Biases into an Attentional Neural Translation Model”, NAACL-HLT. [10] Cohn, Terevor & Lapata, Mirella, (2007) “Machine Translation by Triangulation: Making Effective Use of Multi-Parallel Corpora”.

[11] DeGispert, Adria & Marino, Jose.B, (2006) “Catalan-English statistical machine translation without parallel corpus: bridging through Spanish”.

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[13] El-Kholey, Ahmed & Habash, Nizar, (2013) “Language Independent Connectivity Strength Features for Phrase Pivot Statistical Machine Translation”.

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