Discovery ANALYSIS. Page46. Publication History Received: 23 August 2015 Accepted: 20 September 2015 Published: 12 October 2015

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1 Discovery ANALYSIS The International Daily journal ISSN EISSN Discovery Publication. All Rights Reserved Summarization and Hybrid Machine Translation System for English to Marathi: A Research Effort in Information Retriveal System (H-Machine Translation) Publication History Received: 23 August 2015 Accepted: 20 September 2015 Published: 12 October 2015 Citation Pramod Salunkhe, Mrunal Bewoor, Suhas Patil, Shashank Joshi, Aniket Kadam. Summarization and Hybrid Machine Translation System for English to Marathi: A Research Effort in Information Retriveal System (H-Machine Translation). Discovery, 2015, 43(197), Page46

2 Summarization and Hybrid Machine Translation System for English to Marathi: A Research Effort in Information Retriveal System (H-Machine Translation) Pramod Salunkhe Mrunal Bewoor Suhas Patil Shashank Joshi Aniket Kadam BVDUCOEP BVDUCOEP BVDUCOEP BVDUCOEP PDEA COEM Pune,India Pune,India Pune,India Pune,India Pune,India [email protected] [email protected] [email protected] [email protected] [email protected] Abstract Research issue in information retrieval is retrieving accurate subjective vital information present in one language to user costumed language. Language has been a barrier in information retrieval. The main objective of research effort is to eliminate this gap and present vital foreseen or unseen information to client. A cross lingual machine translation is proposed with summarization as translation as core parts of software product. A person collecting information on a topic may come across text in different languages and would always need summarized and translated information. The proposed system is twin modular PART A summarizes a document extracting important text and Modular PART B Translates it to required language. As Marathi been my local language this system does summarization and then English Assertive and interrogative translation to Marathi. The core methodology in accurate translation is mapping of rules generated from OPEN-NLP package to handcrafted Marathi rules. A parser with lexical analysis methodology search in English Lexicon dataset, if instituted morphological in rank potted. Proposed system is research work in open NLP rule based implementation with translation of source to target language in with summarized text. Language syntactical structure variation and human s natural language. Literature examination reveals that every translation mechanism has some pros and cones and no single technique is better so a hybrid approach is been proposed in translation system. This research is an extended work and ongoing system generated results are been presented Keywords Hybrid Machine Translation,Cross-language Translation,Machine Translation,Open-NLP,Rule-based language spoken and used by people more Than 0.8 billion individuals has been derived from Sanskrit. Word in order is major problem in translation of spring language to objective language. Marathi is mostly spoken language in State of Maharashtra.The structure of language is twin documented from left side to right end, from top end to Bottom end of document. Marathi terms are derived from Sanskrit Nava derived from Navin,month in English Maas derived from Machine. Individuals from different culture and language base are not able to easy communicate where a translation system would facilitate to complete the gap. This research work is directed to first summarize than translate which is useful to Marathi scholar in study of some research work of English writer. In any research work there are numerous issues and problems to address this research work is formulating building hybrid Machine Translation system T= [context {assertive sentence, interrogative sentences}] with summarization to illuminate irrelevant paragraph from document. As research work first script is progressing this is first research article addressing literature Examination and small introduction to proposed work. II. RELATED WORK AND LITERATURE Part A of system is summarization module and part B is translation Many Machine translation(mt) scheme athwart the sphere cover previously are constructed for mainly regularly worn natural tongues that English, Hindi,Chinese, Japanese, Russian etc many more Indian mother tongue languages fig.1 portrays the presented appliance translation schemes and assorted advance used in constructing those schemes. I. INTRODUCTION Machine enabled transformation is core research in Natural Language (NL) for removing language as obstacle in communication and information access with help of bi-lingual machine translation. Research work in Machine translation has been done from English to Hindi, English to Urdu to another language like telgu many native languages and foreign languages like Arabic, Chinese and Spanish. The research problem to address is to community of Marathi language, Fig1. Translation scheme [3] Page47

3 1. Literature Survey on Direct Machine Translation Scheme A) Amongst Indian regional Languages: Anusaaraka scheme Rajeev Sangal at IIT Kanpur started Anusaaraka the aim of project is work in cross lingual translation among Indian languages with spring languages as Marathi Punjabi Bengali Telugu Kannada with translation aimed language Hindi [2]. Section of Indian government for Indian languages technology development (TDIL) department of I.T and in collaboration with satyam I.T firm the scheme is elaborated not for particular domain but translating small children stories.information conservation is main purpose of scheme. Scheme is dependent on spring language grammar structure with output in Hindi language but not perfect grammatically accurate. In for considering Kannada 80% terms in dictionary produced under scheme have 30,000 core terms match to single Hindi term to identify Kannada terms. Scheme manly focuses on inter language access not accuracy Panini and Grammar is been implemented in scheme. B)Punjabi Language to Hindi Translation Scheme G.S.Lehal & G.S. Josan [1] and have urbanized a scheme that is stand on straight term-to-term translation scheme. This scheme encompass of components such as pre-dispensation, term-to-term conversion by means of Punjabi and Hindi word list morphological breakdown, word intellect disambiguation, transliteration and station dispensation. Correctness of version bent by this scheme is 90.67%. Term fault tempo is 2.33% and SER is 24.28%. B) English to Hindi Translation Scheme Patil N and et al [8] urbanized a scheme bottom on relocate foot conversion loom, which employ dissimilar grammatical regulations of spring and aim tongues and a bilingual lexicon for conversion. The translation unit consists of predispensation, English hierarchy initiator, station dispensation of English graph, creation of Hindi graph, Post-dispensation of Hindi graph and produce yield the field of the scheme was climate telling. 3) Machine Translation Scheme: InterTounge A) ANGLABHARTI R M K Sinha, Jain R, Jain A [3][4] urbanized a mechanism support translation scheme intended for converting English to Indian tongues. It is urbanized by means of pretendintertongues loom. The inter-tongue loom complete it probable to employ similar scheme for transform English to additional than solitary Indian tongue and have get rid of require of rising divide translation scheme for English to every Indian Tongue. Investigation of English as spring language is completed only formerly and it fashion midway makeup PLIL (Pseudo Lingua in to Indian tongues). TPLIL is therefore rehabilitated to every Indian Tongues throughout a procedure of text-creation. The effort for PLIL cohort is 70% and transcript generation 30%. Merely through an added 30% effort, novel English to Indian language conversion scheme was built. The effort has be complete whereby do 90% translation chore and outstanding 10% is absent for person post-suppression The field of this machine translation Scheme been in public fitness. C)Hindi-Punjabi conversion scheme on mesh (www) Lehal G S with colleagues [6] urbanized the comprehensive description of Hindi-Punjabi machine conversion scheme on mesh.the scheme has numerous amenities like website conversion, mailbox conversion etc. 2) Transfer-Support Machine Conversion Schemes A) MANTRA (2000) &Mantra MT (1997) Mantra [5] is English to Hindi conversion scheme constructed by Bharati in data conservation scheme. The wording obtainable in solitary Indian tongue is prepared reachable in an additional Indian tongue amid assist of this Scheme. It utilizes XTAG stand wonderful tagger and beam craving examiner urbanized at Pennsylvania for working the examination of effort English copy. It allocates weight on guy and mechanism in fresh techniques. The schemes manufacture numerous yields matching to specified effort. Output support on mainly thorough Examination of English effort passage, incorporates a complete parser and bilingual vocabulary. Parsing scheme is stand on XTAG (Bandyopadhyas-excellent tagger with parser) with slight alteration for job at tender. A client might read yield shaped once full examination, but whilst he determine that scheme have noticeably vanished mistaken or unsuccessful to manufacture the production, he can forever lever to easy output. B) AnglaHindi (2003) AnglaHindi is a copied of AnglaBharti scheme urbanized by R M K Sinha et all for Indian Tongues, that is interlingual regulation-based English to Hindi mechanism- aid conversion Scheme It employ every component of AnglaBharti[4] and in addition employ abstracted instance-foot for translating regularly encounter act noun expression and verb expressions. The correctness of scheme translation is 90%. 4) Hybrid Machine Translation Scheme Anubharti is urbanized using a hybridized [9][8] instance-base mechanism translation loom i.e. a mixture of instance-foot, mass-foot loom and various straightforward grammatical examination. Instance-bottom loom track human-education method for hoard data from past practices and to be worn in future. Anubharti, the customary EBMT (Gupta et all 2003) loom has be customized to reduce the condition of a big instance-bottom the alteration in customary EBMT is accomplish by oversimplify the ingredient and substitute them with inattentive shape from underdone instance. The concept is achieve by recognize syntactic collections. Corresponding of the contribution verdict with inattentive instances is complete stand on syntactic group and semantic labels of spring tongue arrangement. The planning s of together AnglaBharti and AnuBharti, encompass undergone a Page48

4 substantial modify starting their preliminary conceptual design these schemes were forename as AnglaBharti-II and AnuBharti-II in that order. AnglaBharti-II uses a widespread instance-bottom for hybridization in addition a underdone instance-base and the AnuBharti-II creates employ of Hindi as spring tongue for translation to any additional language. The overview of instance-base is reliant ahead the goal language. III. MOTIVATION The motivation of research comes from practical issues faced by person carrying out literature study or collecting information on particular topic person. Language is factor in information access gap.in order to overcome this gap in retrieving appropriate information need s a complete product solution in cross lingual information retrieval.summarization and translation are core modules that facilitate information presentation in each user prefer language. Marathi being a local language with Divergence in pronunciation and very little work is accrued my research work takes motivation from it. is been developed. The OPENNLP package consists of Sentence detect ( ) Tokenization ( ) Parser ( ) Chunk ( ) as in built functions. Dataset of Marathi rules is been developed in mapping to English rules generated by OPENNLP Package. This proposed Six Phase architecture is core design that facilitates accurate information presentation from diverse language to Marathi language.the system is web deployed and web based which extract information from URL s and web pages and various online content.the output os summarization is input to translation module the translated output is cross lingual information retrieved. Architecture of Cross-lingual information retrieval system The architecture of system consists of six phases PHASE1 is summarization module which takes in pdf,doc,txt and web pages information from web. PHASAE2: consists of core summarization module which summarizes based on key word and percentage or centroid based method as user selects PHASE3: This module updates dataset with word and their meaning in Marathi with rules mapping from one language to other. PHASE 4: local and web dataset built information repository which stores data for faster access. PHASE5: this is core Translation module where a selector selects the translation scheme based on user input context example or rule based or in all a combination of all. PHASE 6: WORDNET implementation. Fig1: research Motivation IV. PROPOSED SYSTEM:CORE METHODOLOGY The core module in accurate information presentation is translation hence core methodology employ s mapping one to one rule in English to appropriate rule in Marathi language which are handcrafted in study details of structure and parsing study of OPEN NLP.Summarization methodology is simple and centroid based for topical or subject summarization [11][12] if in research implement context in translation Core methodologyy in part B of system is as shown in fig2. Core Technique is lexical analyzer to detect morphological structure which is matched to English lexicon and then applied to English grammar rule further mapping of English word is been done with Marathi a set of rules are been written to generate exact Marathi sentence translated.the research writer in Marathi studding the English document would be presented with impomation in Marathi and can further carry his work without any information gap. The inbuilt OPENNLP packages are been used to programme the system. A dictionary set is been generated to store in proper nouns pronouns and verbs adverbs.a healthy dataset consists of words terms and phrases Fig 2.Architecture of cross- lingual information retrieval system Page49

5 The core methodology of execution is presented as following in ascending order as: ALGORTHMIC PROCEDURE: SIX PHASE 1. INPUT FROM SUMMARIZATION ( KEYWORD BASED, % BASED AND CENTROID BASED) 2. PROCESS OF TRANSLATION implementation makes the system fully automated in translation.the system is in progress and this are preliminary results. {ADDING PRODUCTION RULES, TOKENIZATION, POS TAGGING, CHUNKING, PARSING ( OPEN NLP FUNCTION) 3. SEARCH THE TOKEN MAPPING ONE TO ONE OF RULE IN ENGLISH TO MARATHI MATCHING RULE 4. SEARCH RULE FROM DATABASE (Not found) 5. CREATE NEW RULE IN WITH WRITING RULES OF OPENNLP (UPDATE DATABASE) 6. IMPLEMENTATION OF WORDNET FOR ACCURACY A. MODULE A SUMMARIZATION 1) Creation of training dataset for Marathi words alphabets. 2) Centroid based methodology is selected for Marathi document summarization 3) Keyword based and percentage summarization applied 4) Generalized summarizations to context based summarize. B. MODULE B H-SCHEME 1) Rule based translation: rule R1 R2 R3. 2) Context based translation. 3) Example based tarnslation 3) Combination of all :Hybrid Translation. V. PRELIMINARY IMPLEMENTATION AND REULTS Base modular design has been implemented with PART A Summarizing doc, pdf docx, text files along with web pages as inputs. Part A summarizes keyword based and percentage based which is been further extended to Centroid based extractive summarization. Extractive summary of PART A is input to PART B current state of work this is rule based translation system which checks for mapping of rules from English to Marathi equivalent rules from dataset. If rule not found it is been constructed and added to dataset. The architecture of Information retrieval system is six phase which facilitates cross lingual information retrieval A selector selects rule based,context based,or example based translation mechanism or in all a hybrid mechanism.wordnet \ Page50

6 {F1} Relevant Sentences but not retrieved =08 Recall=8/ Comparative study for Motivation We consider Google translator to compare the system for given input paragraph consisting of 4 sentences and check the system performance in.the comparison is instance based and solely for motivation purpose we do not wish to compare system with Google as Google has large development system and its not correct to evaluate any other system with it. Writing rule for Marathi equivalent following tabular chart is been referred in writing mapped rules.the rule set on left side correspond to English language while rules on right side correspond to Marathi equivalent rules. This research work is in progress and hence the results presented are preliminary only. Performance Parameters for evaluation of system are been Precision and Recall at current work under implementation we only incorporate this Two comparison Parameters. A data set of 10 documents and 50 sentences have been tested for evaluating system. Precision=correctly retrieved information/total dataset VI. RESEARCH STUDY OUTCOMES AND EXPECTED RESEARCH OUTCOMES:IN PROCESS EXPERIMENTAL RESULTS Information extraction that bears quality has language barriers due to which user remain unknown of good quality information or literature of person which is barrier even for research in history domain or sociology and hence a system is required that supports such research person in summarizing and translating information. This research would build in software system that facilitates research scholars. A software system capable to summarize and translate documents web based application comparable in terms of certain parameters to Google translator. Assertive sentence affirmative sentence generalized translation system to hybrids system Recall= total retrieved inform from dataset/total dataset information. Precision= {TE} /TE+FP Relevant Sentences {TE} =08; Retrieved Sentences {TE+FP} =10 then Precision=8/ Recall= {TP} / {TP}+{F1} Acknowledgment It has been Immense Guidance and direction showing of Prof.M.S.Bewoor that has given me motivation in research work and Dr.D.M.Thakore sir guidance is valuable to us. Guidance of Dr.S.H patil has been valuable in writing this research article.the core Methodology Technique and architecture of Information Retriveal system was formulated Page51

7 by Aniket Kadam. I Express great Thanks to him. The research article writing ppt of Shashank Joshi was valuable in completing research arcticle. I epress thank to him also.at end a thanks to all authors whose articles are been cited in paper. References [1] Bonnie J. Dorr, Pamela W. Jordan, John W. Benoit,A Survey of Current Paradigms in Machine Translation, LAMP TR-027, Dec [2] [3] [4] TRANSLATION OF TELUGU-MARATHI AND VICE-VERSA USING RULE BASED MACHINE TRANSLATION, Dr. Siddhartha Ghosh1, Sujata Thamke2 and Kalyani U.R.S,International journal of information retrieval [5] Subjective and Objective Evaluation of English to Urdu Machine Translation, Vaishali Gupta, Nisheeth Joshi, Iti Mathur, IEEE-ACM [6] Identifying Devnagri characters Karnik, R.R. IEEE Paper [7] Min Zang, Hongfei Jiang, 2008, Grammar comparison study for Translation Equivalence Modeling and Statistical Machine Translation. In the Proceeding of the 22nd International Conference of Computational Linguistics pages [8] Sangal, Rajeev,Dipti Misra Sharma, Lakshmi Bai, Karunesh Arora, Developing Indian languages corpora: Standards and practice, November. [9] Abhijeet R. Joshi, M. Sasikumar, Constructive approach toteachinflectionnmarathilanguage, te/.../pdf.../catiml1.pdf [10] Mining text data C.C Agarwal CX Zhai books.google.com [digital vesion] [11] A survey of text summarization techniques A Nenkova k MCkeown springer 2012 [12] Multi-document multilingual summarization corpus preparation, Arabic, English, Greek, Chinese, Romanian by Lei Li Corina Forascu Proceedings of the MultiLing 2013 Workshop on Multilingual Multi-document Summarization [13] [online ]. [14] Page52