Word Sense Based Hindi-Tamil Statistical Machine Translation

2018 ◽  
Vol 14 (1) ◽  
pp. 17-27
Author(s):  
Vimal Kumar K. ◽  
Divakar Yadav
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Semantic Analysis ◽  
Target Language ◽  
Translation System ◽  
Great Success ◽  
Source Language ◽  
Additional Information ◽  
Part Of Speech ◽  
Preceding Word

Corpus based natural language processing has emerged with great success in recent years. It is not only used for languages like English, French, Spanish, and Hindi but also is widely used for languages like Tamil, Telugu etc. This paper focuses to increase the accuracy of machine translation from Hindi to Tamil by considering the word's sense as well as its part-of-speech. This system works on word by word translation from Hindi to Tamil language which makes use of additional information such as the preceding words, the current word's part of speech and the word's sense itself. For such a translation system, the frequency of words occurring in the corpus, the tagging of the input words and the probability of the preceding word of the tagged words are required. Wordnet is used to identify various synonym for the words specified in the source language. Among these words, the one which is more relevant to the word specified in source language is considered for the translation to target language. The introduction of the additional information such as part-of-speech tag, preceding word information and semantic analysis has greatly improved the accuracy of the system.

Word Sense Based Hindi-Tamil Statistical Machine Translation

2020 ◽  
pp. 410-421
Author(s):  
Vimal Kumar K. ◽  
Divakar Yadav
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Semantic Analysis ◽  
Translation System ◽  
Great Success ◽  
Source Language ◽  
Additional Information ◽  
Part Of Speech ◽  
Preceding Word ◽  
The One

Corpus based natural language processing has emerged with great success in recent years. It is not only used for languages like English, French, Spanish, and Hindi but also is widely used for languages like Tamil, Telugu etc. This paper focuses to increase the accuracy of machine translation from Hindi to Tamil by considering the word's sense as well as its part-of-speech. This system works on word by word translation from Hindi to Tamil language which makes use of additional information such as the preceding words, the current word's part of speech and the word's sense itself. For such a translation system, the frequency of words occurring in the corpus, the tagging of the input words and the probability of the preceding word of the tagged words are required. Wordnet is used to identify various synonym for the words specified in the source language. Among these words, the one which is more relevant to the word specified in source language is considered for the translation to target language. The introduction of the additional information such as part-of-speech tag, preceding word information and semantic analysis has greatly improved the accuracy of the system.

A Knowledge-Based Machine Translation Using AI Technique

2018 ◽  
Vol 6 (3) ◽  
pp. 79-92
Author(s):  
Sahar A. El-Rahman ◽  
Tarek A. El-Shishtawy ◽  
Raafat A. El-Kammar
Keyword(s):  
Machine Translation ◽  
Target Language ◽  
Translation System ◽  
Module Structure ◽  
Source Language ◽  
Word Category ◽  
Knowledge Based ◽  
Language Analysis ◽  
Transfer Rules

This article presents a realistic technique for the machine aided translation system. In this technique, the system dictionary is partitioned into a multi-module structure for fast retrieval of Arabic features of English words. Each module is accessed through an interface that includes the necessary morphological rules, which directs the search toward the proper sub-dictionary. Another factor that aids fast retrieval of Arabic features of words is the prediction of the word category, and accesses its sub-dictionary to retrieve the corresponding attributes. The system consists of three main parts, which are the source language analysis, the transfer rules between source language (English) and target language (Arabic), and the generation of the target language. The proposed system is able to translate, some negative forms, demonstrations, and conjunctions, and also adjust nouns, verbs, and adjectives according their attributes. Then, it adds the symptom of Arabic words to generate a correct sentence.

scholarly journals A Bicolano-to-Tagalog Transfer-Based Machine Translation System

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1324-1330
Keyword(s):  
Machine Translation ◽  
Syntactic Structure ◽  
Internal Representation ◽  
Target Language ◽  
Translation System ◽  
Source Language ◽  
Three Phase ◽  
Transfer Rules ◽  
Machine Translation System ◽  
Overall Performance

The Bicolano-Tagalog Transfer-based Machine Translation System is a unidirectional machine translator for languages Bicolano and Tagalog. The transfer-based approach is divided into three phase: Pre-Processing Analysis, Morphological Transfer, and Sentence Generation. The system analyze first the source language (Bicolano) input to create some internal representation. This includes the tokenizer, stemmer, POS tag and parser. Through transfer rules, it then typically manipulates this internal representation to transfer parsed source language syntactic structure into target language syntactic structure. Finally, the system generates Tagalog sentence from own morphological and syntactic information. Each phase will undergo training and evaluation test for the competence of end-results. Overall performance shows a 71.71% accuracy rate.

scholarly journals Hindi Chhattisgarhi Machine Translation System Using Statistical Approach

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 02) ◽  
pp. 208-222
Author(s):  
Vikas Pandey ◽  
Dr.M.V. Padmavati ◽  
Dr. Ramesh Kumar
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Statistical Approach ◽  
Statistical Machine Translation ◽  
Target Language ◽  
Translation System ◽  
Parallel Corpus ◽  
Machine Translation System ◽  
Unknown Words ◽  
Language Pair

Machine Translation is a subfield of Natural language Processing (NLP) which uses to translate source language to target language. In this paper an attempt has been made to make a Hindi Chhattisgarhi machine translation system which is based on statistical approach. In the state of Chhattisgarh there is a long awaited need for Hindi to Chhattisgarhi machine translation system for converting Hindi into Chhattisgarhi especially for non Chhattisgarhi speaking people. In order to develop Hindi Chhattisgarhi statistical machine translation system an open source software called Moses is used. Moses is a statistical machine translation system and used to automatically train the translation model for Hindi Chhattisgarhi language pair called as parallel corpus. A collection of structured text to study linguistic properties is called corpus. This machine translation system works on parallel corpus of 40,000 Hindi-Chhattisgarhi bilingual sentences. In order to overcome translation problem related to proper noun and unknown words, a transliteration system is also embedded in it. These sentences are extracted from various domains like stories, novels, text books and news papers etc. This system is tested on 1000 sentences to check the grammatical correctness of sentences and it was found that an accuracy of 75% is achieved.

scholarly journals Synthetic Treebanking for Cross-Lingual Dependency Parsing

2016 ◽  
Vol 55 ◽  
pp. 209-248 ◽  
Author(s):  
Jörg Tiedemann ◽  
Zeljko Agić
Keyword(s):  
Machine Translation ◽  
Target Language ◽  
Dependency Parsing ◽  
Practical Applications ◽  
Source Language ◽  
Part Of Speech ◽  
Statistical Dependency ◽  
Target Languages ◽  
Cross Lingual ◽  
The Impact

How do we parse the languages for which no treebanks are available? This contribution addresses the cross-lingual viewpoint on statistical dependency parsing, in which we attempt to make use of resource-rich source language treebanks to build and adapt models for the under-resourced target languages. We outline the benefits, and indicate the drawbacks of the current major approaches. We emphasize synthetic treebanking: the automatic creation of target language treebanks by means of annotation projection and machine translation. We present competitive results in cross-lingual dependency parsing using a combination of various techniques that contribute to the overall success of the method. We further include a detailed discussion about the impact of part-of-speech label accuracy on parsing results that provide guidance in practical applications of cross-lingual methods for truly under-resourced languages.

scholarly journals Language To Language Translation System

2020 ◽  
pp. 289-293
Author(s):  
Ms Pratheeksha ◽  
Pratheeksha Rai ◽  
Ms Vijetha
Keyword(s):  
Speech Recognition ◽  
Machine Translation ◽  
Automatic Speech Recognition ◽  
Speech Synthesis ◽  
Language Translation ◽  
Target Language ◽  
Translation System ◽  
Text To Speech ◽  
Source Language ◽  
Text To Speech Synthesis

The system used in Language to Language Translation is the phrases spoken in one language are immediately spoken in other language by the device. Language to Language Translation is a three steps software process which includes Automatic Speech Recognition, Machine Translation and Voice Synthesis. Language to Language system includes the major speech translation projects using different approaches for Speech Recognition, Translation and Text to Speech synthesis highlighting the major pros and cons for the approach being used. Language translation is a process that takes the conversational phrase in one language as an input and translated speech phrases in another language as the output. The three components of language-to-language translation are connected in a sequential order. Automatic Speech Recognition (ASR) is responsible for converting the spoken phrases of source language to the text in the same language followed by machine translation which translates the source language to next target language text and finally the speech synthesizer is responsible for text to speech conversion of target language.

scholarly journals Bodo to English Machine Translation through Transliteration

2019 ◽  
Vol 8 (12) ◽  
pp. 4825-4830
Keyword(s):  
Natural Language ◽  
Machine Translation ◽  
Computational Linguistics ◽  
Language Processing ◽  
Research Work ◽  
Primary Objective ◽  
Target Language ◽  
Translation System ◽  
Hybrid Technique ◽  
High Resource

Machine Translation (MT) is a technique that automatically translates text from one natural language to another using machine like computer. Machine Transliteration (MTn) is also a technique that converts the script of text from source language to target language without changing the pronunciation of the source text. Both the MT and MTn are the challenging research task in the field of Natural Language Processing (NLP) and Computational Linguistics (CL) globally. English is a high resource natural language, whereas Bodo is a low resource natural language. Though Bodo is a recognized language of India; still not much research work has been done on MT and MTn systems due to the low resources. The primary objective of this paper is to develop Bodo to English Machine Translation system with the help of Bodo to English Machine Transliteration system. The Bodo to English MT system has been developed using the Phrase-based Statistical Machine Translation technique for General and News domains of Bodo-English parallel text corpus. The Bodo to English MTn system has been developed using the Hybrid technique for General and News domains of Bodo-English parallel transliterated words/terms. The translation accuracy of the MT system has been evaluated using BLEU technique

scholarly journals Parse tree-based machine translation for less-used languages

2008 ◽  
Vol 5 (1) ◽  
Author(s):  
Jernej Vičič ◽  
Andrej Brodnik
Keyword(s):  
Empirical Evaluation ◽  
Target Language ◽  
Parse Tree ◽  
Translation System ◽  
Word Alignment ◽  
Training Algorithms ◽  
Source Language ◽  
Pos Tagging ◽  
World Languages ◽  
Part Of Speech

The article describes a method that enhances translation performance of language pairs with a less used source language and a widely used target language. We propose a method that enables the use of parse tree based statistical translation algorithms for language pairs with a less used source language and a widely used target language. Automatic part of speech (POS) tagging algorithms have become accurate to the extent of efficient use in many tasks. Most of these methods are quite easily implementable in most world languages. The method is divided in two parts; the first part constructs alignments between POS tags of source sentences and induced parse trees of target language. The second part searches through trained data and selects the best candidates for target sentences, the translations. The method was not fully implemented due to time constraints; the training part was implemented and incorporated into a functional translation system; the inclusion of a word alignment model into the translation part was not implemented. The empirical evaluation addressing the quality of trained data was carried out on a full implementation of the presented training algorithms and the results confirm the employability of the method.

scholarly journals Inflection rules for Marathi to English in rule based machine translation

2021 ◽  
Vol 10 (3) ◽  
pp. 780
Author(s):  
Namrata G Kharate ◽  
Varsha H Patil
Keyword(s):  
Natural Language Processing ◽  
Machine Translation ◽  
Language Processing ◽  
Important Application ◽  
Target Language ◽  
Rule Based ◽  
Parts Of Speech ◽  
Source Language ◽  
Target Languages ◽  
Correct Translation

Machine translation is important application in natural language processing. Machine translation means translation from source language to target language to save the meaning of the sentence. A large amount of research is going on in the area of machine translation. However, research with machine translation remains highly localized to the particular source and target languages as they differ syntactically and morphologically. Appropriate inflections result correct translation. This paper elaborates the rules for inflecting the parts-of-speech and implements the inflection for Marathi to English translation. The inflection of nouns, pronouns, verbs, adjectives are carried out on the basis of semantics of the sentence. The results are discussed with examples.

scholarly journals Arabic-to-Malay Machine Translation Using Transfer Approach

2020 ◽  
Vol 13 (4) ◽  
pp. 1
Author(s):  
Mohammed M. Abu Shquier
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Target Language ◽  
Average Score ◽  
Human Judgment ◽  
Ongoing Research ◽  
Source Language

Translation from/to Arabic has been widely studied recently. This study focuses on the translation of Arabic as a source language (SL) to Malay as a target language (TL). The proposed prototype will be conducted to map the SL ”meaning”with the most equivalent translation in the TL. In this paper, we will investigate Arabic-Malay Machine Translation features (i.e., syntactic, semantic, and morphology), our proposed method aims at building a robust lexical Machine Translation prototype namely (AMMT). The paper proposes an ongoing research for building a successful Arabic-Malay MT engine. Human judgment and bleu evaluation have been used for evaluation purposes, The result of the first experiment prove that our system(AMMT) has outperformed several well-regarded MT systems by an average of 98, while the second experiment shows an average score of 1-gram, 2-gram and 3-gram as 0.90, 0.87 and 0.88 respectively. This result could be considered as a contribution to the domain of natural language processing (NLP).

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