ESL learners wish to use idiomatic phrases in their discourse as it is an When the literal meaning fails to give clues, they look for figurative. A to Z list of Idioms and phrases with their meanings and examples PDF free download. This lesson you will learn the meaning of some of the idioms from the . in the translation field, known as phrase based translation system. This type of . phrases of English and Tamil, and proverbs of English and. Tamil are also.
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group of words while translating from English to Tamil. This in turn affects the accuracy of the translation. The proposed technique is used to handle the idioms . A List of the most commonly used English idioms. A hot potato Actions speak louder than words. People's Meaning: without any hesitation; instantly. Back to . Learn small English sentences with Tamil meaning | சிறு ஆங்கில Speaking Tips C1-C2 Levels English Phrases, English Idioms, English Words.
If you carry out your plans boldly, the luck is more likely to favor you. Get out while the going getting is good. To leave a place or situation before conditions worsen and it becomes difficult to leave.
Example: With the stock market at an all-time high and further upside looking difficult, we decided to sell our shares and get out while the going was good. If you give someone a small amount of power or freedom to do something, they may try to get a lot more. God helps those who help themselves. Go in one ear and out the other. If something you hear goes in one ear and out the other, you quickly forget it.
Example: Their advice to her went in one ear and out the other. Good things come to those who wait. Patience is often rewarded. Example: The best investors in the world have made their fortunes by investing for the long term. Grief divided is made lighter. Half a loaf is better than none. Getting less than what one wants is better than not getting anything. Example: X: Did you get the compensation for damage to your vehicle?
X: Well, half a loaf is better than none. Honesty is the best policy. Example: I think you should just explain what happened, rather than trying to cover your tracks. Honesty is the best policy, after all.
Hope for the best, prepare for the worst. Be optimistic, but be prepared for a scenario where things can go wrong. Example: X: Why do you want to change this component in the machine when everything is working fine? Y: OK. I agree. Example: I need that book for completing my assignment.
Example: ABC Pvt. If you do something dangerous or adventurous, you may get harmed. Example: Enacting the stunts of movie superheroes in real life is playing with fire. You may get burned.
Ignorance is bliss. This proverb, however, is often used in negative way — ignorance is not bliss. Sometimes, ignorance is bliss. It is easy to understand what you could have done to prevent something bad from happening after it has happened. Example: I would have never bought an apartment if I had known that the land on which it has been built is disputed. Example: X: He is feeling terrible for accidently elbowing the flower pot from the window. If you say something is tip of the iceberg, you mean that thing is just a small part of the entire thing.
It takes two to make a quarrel. Example: X: Why are you always so quarrelsome? It takes two to tango. Where two parties are involved in a situation, fault usually lies with both if things go wrong.
Rarely can one party be blamed entirely. It takes two to tango, after all. Keep your mouth shut and your eyes open. Speak only when necessary and remain alert and observant at all times. So, to avoid problems, keep your mouth shut and your eyes open.
Kill two birds with one stone. Solve two problems with the same action. Example: He killed two birds with one stone by downloading the grocery and visiting the museum on the same route. Laughter is the best medicine. Thinking positively and laughing will help you to feel better. Example: I think the best thing for you right now would be to spend some time with people you can joke around with. Laughter is the best medicine, after all. Learn to walk before you run. Learn basic skills first before venturing into complex things.
Example: X: I want to submit my first article to Fortune magazine for publication. Y: I think you should aim for smaller publications to start with. You should learn to walk before you run.
Let sleeping dogs lie. Example: X: Should I ask the professor if he is upset about my late submission of the assignment? Lightning never strikes twice in the same place. Misfortune does not occur twice in the same way to the same person. Like a fish out of water. To feel awkward because you are in a situation that you have not experienced before. Example: I felt like a fish out of water during my first week in the college, as I hardly knew anyone there.
Look before you leap. I would say look before you leap.
Make such an attractive proposition that it would be foolhardy for anyone to refuse it. Make hay while the sun shines. Make the most of favorable conditions till they last. Example: I got plenty of referral traffic to my website from Facebook in its initial years.
I made hay while the sun shone. Later on they changed their algorithm, after which the traffic dried. Money talks. Money gives one power and influence. Money talks, you know.
Necessity is the mother of invention. A need or problem forces people to come up with innovative solutions. Example: In some parts of the world, farmers use washing machine to clean potatoes in large volumes. Necessity, after all, is the mother of invention. Never put off until tomorrow what you can do today. No gain without pain. It is necessary to suffer or work hard in order to succeed or make progress. No news is good news. Example: My daughter has been working in Australia for nearly five years now.
Once bitten twice shy. Once bitten twice shy, I guess. Sometimes you get so focused on small details that you may miss the larger context. Out of sight, out of mind. Example: Many celebrities find a way to appear in media because they know that out of sight is out of mind.
Paddle your own canoe. Be independent and not need help from anyone. Example: After I went to boarding school in my teens, I started paddling my own canoe to a large extent. Pen is mightier than sword. Thinking and writing have more influence on people and events than use of force.
Example: After the mass killings at the newspaper office, there is a protest which is happening in the city declaring support to the paper and proving that pen is mightier than sword. People who have faults should not criticize other people for having the same faults. Example: The main political party in the opposition has blamed the ruling party for giving tickets to people with dubious background in the upcoming elections.
But the big question is: are they themselves clean on this count? Practice makes perfect. Doing something over and over makes one better at it.
Practice what you preach. Behave the way you encourage other people to behave. Example: You keep telling us to go for a jog in the morning, but I wish you would practice what you preach.
Important work takes time to complete. Silence is half consent. Slow and steady wins the race. Slow and consistent work leads to better chance of success than quick work in spurts.
I guess slow and steady wins the race. Still waters run deep. Example: She is one of the smartest persons in the organization. She may not talk much, but still waters run deep.
Strike while the iron is hot. Take advantage of an opportunity as soon as it comes along. Example: I thought over the job offer I got way too long. Now it has been offered to someone else.
I should have struck while the iron was hot. The best-laid plans go astray. Despite best preparations, things may not go your way. The end justifies the means. A desired result is so important that any method, even a morally bad one, may be used to achieve it. The harder you work, the luckier you get. The harder you work, the more good ideas and chances you may make for yourself.
Example: Many think he got lucky in getting that fat contract, but few know he had been pursuing dozens of such contracts for several weeks — the harder you work, the luckier you get. The grass is greener on the other side of the fence. People are never satisfied with their own situation; they always think others have it better. Example: X: When I see him post all those travel pictures on Instagram, I feel he has the perfect life. I see your thought as grass being greener on the other side of the fence.
The pot is calling the kettle black. People should not criticize someone else for a fault that they themselves have. Example: He accused me of being selfish. Talk about the pot calling the kettle black! The proof of the pudding is in the eating. You can only judge the quality of something after you have tried, used, or experienced it.
Example: X: Marketers have claimed that this weight loss diet produces strong results in just two months. After all, proof of pudding is in the eating. There are more ways than one to skin a cat. There is more than one way to reach the same goal. There is no time like the present.
The best time to do something is right now. So, act now. There is safety in numbers. A group offers more protection than when you are on your own. The road to hell is paved with good intentions. Example: X: Well, I was only trying to be helpful by mixing those two acids. Y: But, it exploded the beaker. Well, the road to hell is paved with good intentions. The show must go on. A performance, event, etc. Example: The chairman died yesterday but the show must go on.
The squeaky wheel gets the grease. People who complain the most are the ones who get attention or what they want. The squeaky wheel gets the grease, after all. The tail is wagging the dog. If the tail is wagging the dog, then a small or unimportant part of something is becoming too important and is controlling the whole thing. Time and tide wait for no man. To know which side your bread is buttered on.
Example: I know which side my bread is buttered on. So, I was very nice to the recruiter and promptly sent her a thank-you card after our interview. Too many cooks spoil the broth. When too many people work together on a project, the result is inferior. What goes around comes around. If someone treats other people badly, he or she will eventually be treated badly by someone else. Example: He tormented me back in high school, and now he has his own bully. When in Rome, do as the Romans do.
When visiting a foreign land, follow the customs of local people. When in Rome, do as the Romans do, right?
When the going gets tough, the tough get going. When conditions become difficult, strong people take action. If you are determined enough, you can find a way to achieve what you want, even if it is difficult. Example: He had little resources to start his business, but he eventually did through a small opening — blog. If there are rumors or signs that something is true so it must be at least partly true. Example: X: Do you believe those rumors about the mayor?
Where one door shuts, another opens. When you lose an opportunity to do one thing, an opportunity to do something else appears. Example: X: I failed to get into my dream college.
Without supervision, people will do as they please, especially in disregarding or breaking rules. You can catch more flies with honey than with vinegar. Example: X: The courier service has taken more time to deliver than they had promised. I want to take the issue up with them and get a refund. Y: I would suggest you deal with them politely. Sometimes you may face disappointments in your pursuits or your wishes may not be fulfilled. Example: X: I want a bike on my birthday. Example: It took me a while, but I eventually understood that I was a round peg in a square hole in the firm.
It is hard to achieve something important without causing unpleasant effects. The transfer at the word level exploits the similarities found in the structure of Indian languages. It is a domain specific translation system, which aims to transfer English text into Hindi. It basically follows Angla Bharati approach.
It concentrates on the translation of administrative languages. A project headed by Mr. The first phase is over and the second phase is going on. Tamil university has built a translation system to translate between Russian language and Tamil. Kamshi discusses elaborately about the structural differences of English and Tamil and she has made use of lexical-transfer approach to build an aid to translate English text books in English into Tamil.
She has listed a series of transfer rules and build a elaborate bilingual dictionary to serve her purpose. The details of the previous works are given elaborately in the second chapter.
It discusses about the aims and objectives, methodology, earlier works in the field of investigation and the uses of the present research work. Building rule based machine translation systems are time consuming and uneconomical.
So the best alternative is to build Statistical based machine translation system using parallel corpus. The present work is only a starting point. With the availability of huge English-Tamil parallel corpus the system will improve and supersede Google English-Tamil on-line translation system which is founded on the same ground. Chapter -2 Survey of MT systems in India and abroad 2. Machine Translation MT mainly deals with transformation of one language to another.
Coming to the MT scenarios in India, it has enormous scope due to many regional languages of India. It is pertinent that majority of the population in India are fluent in regional languages such as Hindi, Punjabi etc.. Given such a scenario, MT can be used to provide an interface of regional language. Machine Translation is the process of using computers to automate some or all of the process of translation from one language to another. It is an area of applied research that draws ideas and techniques from linguistics, computer science, artificial intelligence, translation theory, and statistics.
It is a focused field of research in linguistic concepts of syntax, semantics, pragmatics and discourse, computational-linguistic approaches such as parsing algorithms, semantic and pragmatic clarification and text generation, descriptive linguistics that deals with lexicon and language rules for particular languages and modeling human knowledge representation and manipulation. Research began in this field as early as in the late s, and numerous methods some based on extensive linguistic theories and some ad-hoc have been tried over the past five decades.
Machine translation can also be defined as, the application of computers to the task of translating texts from one natural language to another. Today a number of systems are available that are capable of producing translations which, even though not perfect, is of sufficient quality to use in a number of specific domains. In the process of translation, which either carried out manually or automated through machines, the context of the text in the source language when translated must convey the exact context in the target language.
While seeing from the surface, this seems straightforward, but it is far more difficult. Translation is not a just a word level replacement. Also he should be familiar with all the issues during the translation process and must know how to handle it. This requires widespread knowledge in grammar, sentence structure, meanings, etc.
It will be a great challenge for human to face various challenges in the designing a machine translation system, proficient of translating sentences by taking consideration of all the required information to perform translation. Even though, no two individual human translators can generate similar translations of the same text in the same language pair and it may take several revisions to make the translation perfect.
Hence it will be a greater challenge for humans to design a fully automated machine translation system to produce quality translations. This section briefly discusses some of the existing Machine Translation systems and the approaches that have been followed Hutchins, , , ; Solcum Georgetown Automatic Translation GAT System , developed by Georgetown University, used direct approach for translating Russian texts mainly from physics and organic chemistry to English. The GAT strategy was simple word- for-word replacement, followed by a limited amount of transposition of words to result in something vaguely resembling English.
There was no true linguistic theory underlying the GAT design. It had only six grammar rules and items in its vocabulary.
The translation was done using IBM mainframe computer. The experiment was a great success and ushered in an era of Machine Translation research. The Georgetown MT project was terminated in the mids.
It was developed at Grenoble University in France. It is based on Interlingua approach with dependency-structure analysis of each sentence at the grammatical level and transfer mapping from one language- specific meaning representation at the lexical level.
During the period of 71, this system was used to translate about 4,00, words of Russian mathematics and physics texts into French. It was found that it fails for those sentences for which complete analysis cannot be derived. Indirect translation was performed in 14 steps of global analysis, transfer, and synthesis. The performance and accuracy of the system was moderate. Air Force. Translation was word by word, with occasional backtracking, Each Russian item either stem or ending in the lexicon was accompanied by its English equivalent and grammatical codes indicating the classes of stems and affixes that could occur before and after it.
In addition to lexical entries, processing instructions were also intermixed in the dictionary: A third of the entries were phrases, and there was also an extensive system of micro glossaries. An average translation speed of 20 words per second was claimed.
Logos analyzes whole source sentences, considering morphology, meaning, and grammatical structure and function. The analysis determines the semantic relationships between words as well as the syntactic structure of the sentence. Parsing is only source language-specific and generation is target language-specific. Unlike other commercial systems the Logos system relies heavily on semantic analysis. This comprehensive analysis permits the Logos system to construct a complete and idiomatically correct translation in the target language.
This Internet-based system allows users to submit formatted documents for translation to their server and retrieve translated documents without loss of formatting. In , It was used by the U. Air Force to translate English maintenance manuals for military equipment into Vietnamese.
This system reached the commercial market, and has been downloadd by several multi-national organizations e. It was developed at University of Montreal. After short span of time, the domain for translation shifted to translating aviation manuals by adding semantic analysis module to the system.
The overall design of the system is based on the assumption that translation rules should not be applied directly to the input string, but rather to a formal object that represents a structural description of the content of this input.
Thus, the source language SL text or successive fragments of it is mapped onto the representations of an intermediate language, also called normalized structure prior to the application of any target language-dependent rule.
In this system, the dictionaries list only the base form of the words roughly speaking, the entry form in a conventional dictionary. In March , the source language English dictionary included entries; these entries represented the core vocabulary of maintenance manuals, plus a portion of the specialized vocabulary of hydraulics. Of these, had a corresponding entry in the bilingual English-French dictionary.
The system was evaluated and the low accuracy of the translation by the system forced the Canadian Government to cancel the funding and thus TAUM project in The system was originally built for English-Russian Language Pair. Large number of Russian scientific and technical documents were translated using this system. The quality of the translations, although only approximate, was usually adequate for understanding content.
The quality for this purpose was not adequate but improved after adding lexicon entries specific to CEC related translation tasks. GM's English-French dictionary had been expanded to over 1,30, terms by Sereda Sereda reported a speed-up of times in the productivity of his human translators. Sentences are analyzed and translated one at a time in a series of passes.
After each pass, a portion of the sentence is translated into English. The CULT includes modules like source text preparation, input via Chinese keyboard, lexical analysis, syntactic and semantic analysis, relative order analysis, target equivalence analysis, output and output refinement.
It was developed at Brigham Young University. It is an Interactive Translation System that performs global analysis of sentences with human assistance, and then performs indirect transfer again with human assistance. But this project was not successful and hence not operational. METEO scans the network traffic for English weather reports, translates them directly into French, and sends the translations back out over the communications network automatically.
This system is based on the TAUM technology as discussed earlier. Rather than relying on post-editors to discover and correct errors, METEO detects its own errors and passes the offending input to human editors and output deemed correct by METEO is dispatched without human intervention. The title sentences of scientific and engineering papers are analyzed by simple parsing strategies.
Title sentences of physics and mathematics of some databases in English are translated into Japanese with their keywords, author names, journal names and so on by using fundamental structures. The system used transfer based architecture.
It was terminated in due to lack of funds. The system had a main dictionary of about 8, words, accompanied by transducing dictionary covering another 2, words. The typical steps followed in the system are Czech morphological analysis, syntactico semantic analysis with respect to Russian sentence structure and morphological synthesis of Russian. Due to close language pair, a transfer-like translation scheme was adopted with many simplifications.
Also many ambiguities are left unresolved due to the close relationship between Czech and Russian. No deep analysis of input sentences was performed.
There are two main factors that caused a deterioration of the translation. PONS , an experimental interlingua system for automatic translation of unrestricted text, constructed by Helge Dyvik, Department of Linguistics and Phonetics, University of Bergen.
PONS exploits the structural similarity between source and target language to make the shortcuts during the translation process. The system makes use of a lexicon and a set of syntactic rules. There is no morphological analysis. The source text is divided into substrings at certain punctuation marks, and the strings are parsed by a bottom-up, unification-based active chart parser. The system had been tested on translation of sentence sets and simple texts between the closely related languages Norwegian and Swedish, and between the more distantly related English and Norwegian.
It was developed by Marote R. It is a classical indirect Machine Translation system using an advanced morphological transfer strategy. The system has eight modules: This system achieved great speed through the use of finite-state technologies. The Catalan to Spanish is less satisfactory as to vocabulary coverage and accuracy. It translates simple English sentences into equivalent Filipino sentences at the syntactic level. It involves morphological and syntactical analyses, transfer and generation stages.
The whole translation process involves only one sentence at a time. It has three stages: Analysis, Transfer and Generation. Each stage uses bilingual from Tagalog to Cebuano lexicon and a set of rules. The author describes that a new method is used in the POS-tagging process but does not handle ambiguity resolution and is only limited to a one-to-one mapping of words and parts-of-speech.
The syntax analyzer accepts data passed by the POS tagger according to the formal grammar defined by the system. Transfer is implemented through affix and root transfers. The rules used in morphological synthesis are reverse of the rules used in morphological analysis. Result of the evaluation gives a score of good performance 0.
The hybrid approach transfers a Turkish sentence to all of its possible English translations, using a set of manually written transfer rules. Then, it uses a probabilistic language model to pick the most probable translation out of this set.
The accuracy comes out to be about It has been fully implemented for Czech to Slovak, the pair of two most closely related Slavic languages.
The main aim of the system is localization of the texts and programs from one source language into a group of mutually related target languages.
In this system, no deep analysis had been performed and word- for-word translation using stochastic disambiguation of Czech word forms has been performed. The input text is passed through different modules namely morphological analyzer, morphological disambiguation, Domain related bilingual glossaries, general bilingual dictionary, and morphological synthesis of Slovak.
The dictionary covers over 7, 00, items and it is able to recognize more than 15 million word-forms. Work is in progress on translation for Czech-to-Polish language pairs.
Bulgarian-to-Polish Machine Translation system , has been developed by S. This system has been developed based on the approach followed by PONS discussed above.
The system needs a grammar comparison before the actual translation begins so that the necessary pointers between similar rules are created and system is able to determine where it can take a shortcut. The system has three modes, where mode 1 and 2 enable system to use the source language constructions and without making a deeper semantic analysis to translate to the target language construction.
Mode 3 is the escape hatch, when the Polish sentences have to be generated from the semantic representation of the Bulgarian sentence. The accuracy of the system has been reported to be It is in general disambiguated word for word translation. One-to-one translation of words is done using a bilingual dictionary between Turkish and Crimean Tatar. The system accuracy can be improved by making word sense disambiguation module more robust.
Antonio M. The Machine Translation architecture uses finite-state transducers for lexical processing, hidden Markov models for part-of-speech tagging, and finite-state based chunking for structural transfer. Carme Armentano-oller et. Corbi-Bellot et. They use the XML format for linguistic data used by the system. They define five main types of formats for linguistic data i.
Apertium , developed by Carme Armentano-oller et. This platform was developed with funding from the Spanish government and the government of Catalonia at the University of Alicante. Apertium originated as one of the Machine Translation engines in the project OpenTrad and was originally designed to translate between closely related languages, although it has recently been expanded to treat more divergent language pairs such as English—Catalan.
Apertium uses finite-state transducers for all lexical processing operations morphological analysis and generation, lexical transfer , hidden Markov models for part-of-speech tagging, and multi-stage finite-state based chunking for structural transfer. The accuracy has been reported to be It was developed by Gonzalez et. This project tried to combine knowledge-based and corpus-based techniques to produce a Spanish-to- Catalan Machine Translation system with no semantic constraints.
Spanish and Catalan are languages belonging to the Romance language family and have a lot of characteristics in common. SisHiTra makes use of their similarities to simplify the translation process. The system is based on finite state machines. It has following modules: The word error rate is claimed to be Instead of designing translators for English to each Indian language, Anglabharti uses a pseudo-interlingua approach.
This is the basic translation process translating the English source language to PLIL with most of the disambiguation having been performed. The project has been applied mailnly in the domain of public health.
Where there are differences between the languages, the system introduces extra notation to preserve the information of the siurce language.
The output generated is understandable but not grammatically correct. For example, a Bengali to Hindi Anusaaraka can take a Bengali text and produce output in Hindi which can be understood by the user but will not be grammatically perfect. The translation is obtained by matching the input sentences with the minimum distance example sentences.
This made the example-base smaller in size and its further processing partitioning reduces the search space. This approach works more efficiently for similar languages such as among Indian languages.
The Mantra MAchiNe assisted TRAnslation tool translates English text into Hindi in a specified domain of personal administration specifically gazette notifications pertaining to government appointments, office orders, office memorandums and circulars. In addition to translating the content, the system can also preserve the formatting of input word documents across the translation.
This project has also been extended for Hindi-English and Hindi-Bengali language pairs and also existing English- Hindi translation has been extended to the domain of parliament proceeding summaries. MAT , a machine assisted translation system for translating English texts into Kannada, has been developed by Dr.
Keeping this structure in mind, a suitable structure for the equivalent sentence in the target language is first developed. For each word, a suitable target language equivalent is obtained from the bilingual dictionary. The MAT System provides for incorporating syntactic and some simple kinds of semantic constraints in the bilingual dictionary. Finally, the target language sentence is generated by placing the clauses and the word groups in appropriate linear order, according to the constraints of the target language grammar.
Post Editing tool has been provided for editing the translated text. MAT System 1. It has been applied to the domain of government circulars, and funded by the Karnataka government. An English—Hindi Translation System with special reference to weather narration domain has been designed and developed by Lata Gore et. The system is based on transfer based translation approach. MT system transfers the source sentence to the target sentence with the help of different grammatical rules and also a bilingual dictionary.
The translation module consists of sub modules like Pre-processing of input sentence, English tree generator, post-processing of English tree, generation of Hindi tree, Post-processing of Hindi tree and generating output. The translation system gives domain specific translation with satisfactory results. By modifying the database it can be extended to other domains. It involves Machine Translation of bilingual texts at sentence level. In addition, it also includes preprocessing and post-processing tasks.
The longer input sentence is fragmented at punctuations, which results in high quality translation. The results when tested by authors are fascinating with quality translation.
During the development phase, when it is found that the modification in the rule-base is difficult and may result in unpredictable results, the example-base is grown interactively by augmenting it. It incorporated an error-analysis module and statistical language-model for automated post-editing.
Automated pre- editing may even fragment an input sentence if the fragments are easily translatable and positioned in the final translation. Such fragmentation may be triggered by in case of a failure of translation by the 'failure analysis' module. The failure analysis consists of heuristics on speculating what might have gone wrong. The entire system is pipelined with various sub- modules. All these have contributed significantly to greater accuracy and robustness to the system.
The system has been applied mainly in the domain of news, annual reports and technical phrases. The system used rule-bases and heuristics to resolve ambiguities to the extent possible. It has a text categorization component at the front, which determines the type of news story political, terrorism, economic, etc. Depending on the type of news, it uses an appropriate dictionary. It requires considerable human assistance in analyzing the input. Another novel component of the system is that given a complex English sentence, it breaks it up into simpler sentences, which are then analyzed and used to generate Hindi.
The system can work in a fully automatic mode and produce rough translations for end users, but is primarily meant for translators, editors and content providers. The example-based approaches emulate human-learning process for storing knowledge from past experiences to use it in future.
It also uses a shallow parsing of Hindi for chunking and phrasal analysis. The input Hindi sentence is converted into a standardization form to take care of word-order variations. The standardized Hindi sentences are matched with a top level standardized example-base. In case no match is found then a shallow chunker is used to fragment the input sentence into units that are then matched with a hierarchical example-base.
The translated chunks are positioned by matching with sentence level example base. Human post-editing is performed primarily to introduce determiners that are either not present or difficult to estimate in Hindi. It has already produced output from English to three different Indian languages — Hindi, Marathi, and Telugu. It combines rule based approach with statistical approach. Although the system accommodates multiple approaches, the backbone of the system is linguistic analysis. The system consists of 69 different modules.
About 9 modules are used for analyzing the source language English , 24 modules are used for performing bilingual tasks such as substituting target language roots and reordering etc. The overall system architecture is kept extremely simple. All modules operate on a stream of data whose format is Shakti standard format SSF.
This system uses English-Telugu lexicon consisting of 42, words. A word form synthesizer for Telugu is developed and incorporated in the system. It handles English sentences of a variety of complexity. It also used verb sense disambiguator based on verbs argument structure. During translation, the input headline is initially searched in the direct example base for an exact match.
If a match is obtained, the Bengali headline from the example base is produced as output. If there is no match, the headline is tagged and the tagged headline is searched in the Generalized Tagged Example base.
If a match is not found, the Phrasal example base will be used to generate the target translation. If the headline still cannot be translated, the heuristic translation strategy applied is - translation of the individual words or terms in their order of appearance in the input headline will generate the translation of the input headline.
Appropriate dictionaries have been consulted for translation of the news headline. Hinglish , a Machine Translation system for pure standard Hindi to pure English forms developed by R. Mahesh K. Sinha and Anil Thakur. Only in case of polysemous verbs, due to a very shallow grammatical analysis used in the process, the system is unable to resolve their meaning. This system is based on Anusaaraka Machine Translation System architecture.
Stand-alone, API, and Web-based on-line versions have been developed. It includes exhaustive syntactical analysis. Currently, it has limited vocabulary and small set of Transfer rules. AnglaHindi besides using all the modules of AnglaBharti, also makes use of an abstracted example-base for translating frequently encountered noun phrases and verb phrasals. Now, the approach has been changed to statistical Machine Translation between English and Indian languages.
It is based on a bilingual dictionary comprising of sentence-dictionary, phrases-dictionary, words-dictionary and phonetic- dictionary and is used for the Machine Translation. Each of the above dictionaries contains parallel corpora of sentences, phrases and words, and phonetic mappings of words in their respective files.
These sentences have been manually translated into three of the target Indian languages, namely Hindi, Kannada and Tamil. Google Translate , is based on statistical Machine Translation approach, and more specifically, on research by Franz-Josef Och.
Currently, it is providing the facility of translation among 51 language pairs. It includes only one Indian language Hindi. The accuracy of translation is good enough to understand the translated text. This system is based on direct word-to-word translation approach. The system has reported Machine Translation System among Indian languages , developed by the Consortium of Institutions. The accuracy of the translation is not up to the mark.
Bable Fish , developed by AltaVista, is a web-based application on Yahoo! All translation pairs are powered by Microsoft Translation previously Systran , developed by Microsoft Research, as its backend translation software. The translation service is also using statistical Machine Translation strategy to some extent [Internet Source: This system uses multi-engine Machine Translation approach.
The BLUE score obtained during system evaluation is 0. But it was only in the 20th century, the first concrete proposals to machine translation have been made by George Artsrouni, a French-Armenian and by Petr Smirnov-Troyanskii, a Russian, independently in Artsrouni designed a storage device on paper tape which could be used to find the equivalent of any word in another language; a prototype was apparently demonstrated in Troyanskii envisioned the three stages of mechanical translation: He also envisioned both the bilingual and multilingual translation.
Even though, in his idea the role of machine lies only in the second stage, he said that the logical analysis will be also automated, in the years to come. In this experiment, a carefully selected sample of 49 Russian sentences was translated into English, using a very restricted vocabulary of words and just 6 grammar rules.
The experiment was a great success and ushered in an era of substantial funding for machine-translation research. The decade of — was considered as a decade of high expectations and also the decade which destroyed the false belief that the problem of machine translation could be solved in just a few years. This was mainly because most of the people in this area of research, aimed at developing immediate systems for translation without considering the various issues in machine translation.
But it was too late when they understood that it was impossible to produce translation systems over a short span of time.
The problem of disillusion increased as the linguistic complexity gets more and more apparent. As the progress shown by the researchers was very much slower and also as it failed to fulfill the expectations of the governments and companies, who funded their research, the government sponsors of MT in the United States formed the Automatic Language Processing Advisory Committee ALPAC to examine the prospects in It concluded in its famous report that machine translation was slower, less accurate and twice as expensive as human translation and that there is no immediate or predictable prospect of useful machine translation.
It saw no need for further investment in machine translation research; instead it recommended the development of machine aids for translators, such as automatic dictionaries, and continued support of basic research in computational linguistics. It is true that it failed to recognize, for example, that revision of manually produced translations is essential for high quality, and it was unfair to criticize machine translation for needing to post- edit output. It may also have misjudged the economics of computer-based translation, but large-scale support of current approaches could not continue.
It brought a virtual end to machine translation research in the USA for over a decade and MT was for many years perceived as a complete failure. After the ALPAC report, as United States concentrated mainly on translating the Russian's scientific and technical materials and as the need for machine translation has increased in Europe and Canada, the focus of machine translation research switched from the United States to Europe and Canada.
The decade of — , was considered to be a quite decade in the history of machine translation. Research after the mid- s had three main strands: In the latter part of the s developments in syntactic theory, in particular unification grammar, Lexical Functional Grammar and Government Binding theory, began to attract researchers, although their principal impact was to come in the s.
At the time, many observers believed that the most likely source of techniques for improving machine translation quality lay in research on natural language processing within the context of artificial intelligence. The dominant framework of machine translation research until the end of the s was based on essentially linguistic rules of various kinds: The rule-based approach was most obvious in the dominant transfer systems such as Ariane, Metal, SUSY, Mu and Eurotra, but it was at the basis of all the various interlingua systems - both those which were essentially linguistics-oriented such as DLT and Rosetta, and those which were knowledge- based.
Firstly, a group from IBM published in the results of experiments on a system based purely on statistical methods.
The effectiveness of the method was a considerable surprise to many researchers and has inspired others to experiment with statistical methods of various kinds in subsequent years. Secondly, at the very same time certain Japanese groups began to publish preliminary results using methods based on corpora of translation examples, i.
For both approaches the principal feature is that no syntactic or semantic rules are used in the analysis of texts or in the selection of lexical equivalents. Statistical methods were common in the earliest period of machine translation research, in the s, but the results had been generally disappointing. With the success of newer stochastic techniques in speech recognition, the IBM team at Yorktown Heights began to look again at their application to machine translation. The distinctive feature of Candide is that statistical methods are used as virtually the sole means of analysis and generation; no linguistic rules are applied.
The IBM research is based on the vast corpus of French and English texts contained in the reports of Canadian parliamentary debates i. The essence of the method is first to align phrases, word groups and individual words of the parallel texts, and then to calculate the probabilities that any one word in a sentence of one language corresponds to a word or words in the translated sentence with which it is aligned in the other language.
Most researchers were surprised, particularly those involved in rule-based approaches, by the results which were so acceptable: Obviously, the researchers have sought to improve these results, and the IBM group proposes to introduce more sophisticated statistical methods, but they also intend to make use of some minimal linguistic information, e.
The second major corpus-based approach - benefiting likewise from improved rapid access to large databanks of text corpora is what is known as the example- based or memory-based approach.
Although first proposed in by Makoto Nagao, it was only towards the end of the s that experiments began, initially in some Japanese groups and during the DLT project. The underlying hypothesis is that translation often involves the finding or recalling of analogous examples, i.
For calculating matches, some MT groups use semantic methods, e. Other groups use statistical information about lexical frequencies in the target language. The main advantage of the approach is that since the texts have been extracted from databanks of actual translations produced by professional translators there is an assurance that the results will be accurate and idiomatic.
Although the main innovation since has been the growth of corpus- based approaches, rule-based research continues in both transfer and interlingua systems. For example, a number of researchers involved in Eurotra have continued to work on the theoretical approach developed, e.
One consequence of developments in example-based methods has been that much greater attention is now paid to questions of generating good quality texts in target languages than in previous periods of machine translation activity when it was commonly assumed that the most difficult problems concerned analysis, disambiguation and the identification of the antecedents of pronouns.
In part, the impetus for this research has come from the need to provide natural language output from databases, i. Some machine translation teams have researched multilingual generation. The use of machine translation accelerated in the s.
The increase has been most marked in commercial agencies, government services and multinational companies, where translations are produced on a large scale, primarily of technical documentation. This is the major market for the mainframe systems: All have installations where translations are being produced in large volumes. Indeed, it has been estimated that in over million words a year were translated by such services: The literary work is fed to the MT system and translation is done.
Such MT systems can break the language barriers by making available work rich sources of literature available to people across the world. MT also overcomes the technological barriers. This has lead to digital divide in which only small section of society can understand the content presented in digital format. MT can help in this regard to overcome the digital divide. Some of these issues are as follows. Some classification can be done by naming the typical order of subject S , verb V and object O in a sentence.
Some languages have word orders as SOV. The target language may have a different word order. In such cases, word to word translation is difficult. The selection of right word specific to the context is important. Unresolved references can lead to incorrect translation. This was the type of MT envisaged by the pioneers.
This came in with the need to translate military technological documents. The translation output can be considered only as brush-up so that the professional translator can be freed from that boring and time consuming task. This type of machine translation system is usually incorporated into the translation work stations and the PC based translation tools.
But mainly three approaches are used. These are discussed below: Linguistic knowledge will be required in order to write the rules for this type of approaches. These rules will play a vital role during the different levels of translation.
The benefit of rule based machine translation method is that it can intensely examine the sentence at its syntax and semantic levels. There are complications in this method such as prerequisite of vast linguistic knowledge and very huge number of rules is needed in order to cover all the features in a language.
The three different approaches that require linguistic knowledge are as follows: Direct MT 2. Interlingua MT 3. Transfer MT 2. Direct MT form of MT is the most basic one. It translates the individual words in a sentence from one language to another using a two-way dictionary. It makes use of very simple grammar rules. These systems are based upon the principle that as MT system should do as little work as possible.
Direct MT systems take a monolithic approach towards development, i. Direct MT has following characteristics: The direct MT system starts with morphological analysis.
Morphological analysis removes morphological inflections from the words to get the root word from the source language words. A bilingual dictionary is looked up to get the target- language words corresponding to the source-language words. The last step in direct MT system is syntactic rearrangement. In syntactic rearrangement, the word order is changed to that which best matches the word order of the target language.
Figure 2. Direct Machine Translation Direct Machine Translation works well with languages which have same default sentence structure. It does not consider structure and relationships between words. The Interlingua Machine Translation converts words into a universal language that is created for the MT simply to translate it to more than one language.
Whenever a sentence matches one of the rules, or examples, it is translated directly using a dictionary. It goes from the source language to a morphological and syntactic analysis to produce a sort of Interlingua on the base forms of the source language, from this it translates it to the base forms of the target language and from there a better translation is made to create the final step in the translation.
The steps which are performed are shown in Figure 2.
Analysis phase is used to produce source language structure. Transfer phase is used to transfer source language representation to a target level representation. Generation phase is used to generate target language text using target level structure.
The only resource required by this type of approaches is data either the dictionaries for the dictionary based approach or bilingual and monolingual corpus for the empirical or corpus based approaches. In this approach, word level translations will be done. This kind of approach can be used to translate the phrases in a sentence and found to be least useful in translating a full sentence. This approach will be very useful in accelerating the human translation, by providing meaningful word translations and limiting the work of humans to correcting the syntax and grammar of the sentence.
But a bilingual corpus of the language pair and the monolingual corpus of the target language are required to train the system to translate a sentence.
This approach has driven lots of interest world-wide, from late s till now. That is, normally the humans split the problem into sub problems, solve each of the sub problems with the idea of how they solved this type of similar problems in the past and integrate them to solve the problem in whole. This approach needs a huge bilingual corpus of the language pair among which translation has to be performed.
Assuming that we are using a corpus that contains the following two sentence pairs: English Tamil He bought a book He has a car The parts of the sentence to be translated will be matched with these two sentences in the corpus.
Therefore, the corresponding Tamil part of the matched segments of the sentences in the corpus are taken and combined appropriately. Sometimes, post-processing may be required in order to handle numbers, gender if exact words are not available in the corpus. This approach differs from the other approaches to machine translation in many aspects. That is large amount of machine readable natural language texts are available with which this approach can be applied. This approach makes use of translation and language models generated by analysing and determining the parameters for these models from the bilingual corpora and monolingual corpus of the target language, respectively.
In order obtain better translations from this approach, at least more than two million words if designing the system for a particular domain and more than this for designing a general system for translating particular language pair. Moreover, statistical machine translation requires an extensive hardware configuration to create translation models in order to reach average performance levels. Commercial translation systems such as Asia Online and Systran provide systems that were implemented using this approach.
Hybrid machine translation approaches differ in many numbers of aspects: Here the rule based machine translation system produces translations for a given text in source language to text in target language. The output of this rule based system will be post-processed by a statistical system to provide better translations.
However, a machine translation system is solely responsible for the complete translation process from input of the source text to output of the target text without human assistance, using special programs, comprehensive dictionaries, and collections of linguistic rules.
Machine translation occupies the top range of positions on the scale of computer translation ambition. Machine aided translation systems fall into two subgroups: Machine-aided human translation refers to a system wherein the human is responsible for producing the translation per sentence, but may interact with the system in certain prescribed situations - for example, requesting assistance in searching through a local dictionary or thesaurus, accessing a remote terminology data bank, retrieving examples of the use of a word or phrase, or performing word processing functions like formatting.
Indeed the data bank may not be accessible to the translator on-line at all, but may be limited to the production of printed subject-area glossaries. A terminology data banks offers access to technical terminology, but usually not to common words. The chief advantage of terminology data banks is not the fact that it is automated even with on-line access, words can be found just as quickly in a printed dictionary, but that it is up-to date: It is also possible for terminology data banks to contain more entries because it can draw on a larger group of active contributors, its users.
The time duration to design a statistical machine translation system will be very much less when compared to the rule based systems. The advantages of statistical machine translation over rule based machine translation are stated below: In contrast, rule based machine translation system requires a great deal of knowledge apart from the corpus that only linguistic experts can generate, for example, shallow classification, syntax and semantics of all the words of source language in addition to the transfer rules between source and target languages.
Generalizing the rules is more tedious task and hence, multiple rules have to be defined for each case, particularly for languages which have different sentence structure pattern. In the other hand, rule based machine translation systems involves more improvement and customization costs till it touches the anticipated quality threshold. Updated rule based systems will be available at the moment when a person downloads a rule based system from the market.
In particular, rule based systems organisation is generally a time consuming progression including more human resources. Whereas rule based systems have to be redesigned or retrained by the addition of new rules and words to the dictionary amid of many other things, which results in more time consumption and requires more knowledge from the linguists.
Though rule based systems have not found the syntactic information of words suitable for analysing the source language, or does not know the word, which will prevent the finding of suitable rule. Concerning the rule based systems governed by the linguistic rules; they are considered as distinct case of statistical approach. However, if the rules are generalized to a large extent, they will not be able handle rule exceptions. Whereas, various versions of rule based systems generates more alike translations.
Since the situation has changed. Corporate use of machine translation with human assistance has continued to expand particularly in the area of localisation and the use of translation aids has increased particularly with the approaching of translation memories. But the main change has been the ever expanding use of unrevised machine translation output, such as online translation services provided by Babel Fish, Google, etc.
The following states the various applications of machine translation briefly. For most of that history — at least 40 years — it was assumed that there were only two ways of using machine translation systems.
The first was to use machine translation to produce publishable translations, generally with human editing assistance i. The second was to offer the rough unedited machine translation versions to readers able to extract some idea of the content i. In neither case were translators directly involved — machine translation was not seen as a computer aid for translators.
The first machine translation systems operated on the traditional large-scale mainframe computers in large companies and government organizations. There was opposition from translators particularly those with the task of post- editing but the advantages of fast and consistent output has made large- scale machine translation cost-effective.
In order to improve the quality of the raw machine translation output many large companies included methods of controlling the input language by restricting vocabulary and syntactic structures — by such means, the problems of disambiguation and alternative interpretations of structure could be minimised and the quality of the output could be improved.
For most of machine translation history, translators have been wary of the impact of computers in their work. Many saw machine translation as a threat to their jobs — little knowing the inherent limitations of machine translation. During the s and s the situation changed. Translators were offered an increasing range of computer aids.
First came text-related glossaries and concordances, word processing on increasingly affordable microcomputers, then terminological resources on computer databases, access to Internet resources, and finally translation memories. The idea of storing and retrieving already existing translations arose in the late s and early s, but did not come to fruition until the availability of large electronic textual databases and with facilitating bilingual text alignment.
All translators are now aware of their value as cost-effective aids, and they are increasingly asking for systems which go further than simple phrase and word matching — more machine translation - like facilities in other words.
With this growing interest, researchers are devoting more efforts to the real computer-based needs of translators. As just two examples there are the TransSearch and TransType systems: From the middle of the s onwards, mainframe and PC translation systems have been joined by a range of other types. First should be mentioned the obvious further miniaturisation of software: Many, such as the Ectaco range of special devices, are in effect computerized versions of the familiar phrase-book or pocket dictionary, and they are marketed primarily to the tourist and business traveller.
The dictionary sizes are often quite small, and where they include phrases, they are obviously limited. However, they are sold in large numbers and for a very wide range of language pairs. Users may be able to ask their way to the bus station, for example, but they may not be able to understand the answer. Recently, since early in this decade, many of these hand-held devices have included voice output of phrases, an obvious attraction for those unfamiliar with pronunciation in the target language.
There is an increasing number of phrase-book systems offer voice output. This facility is also increasingly available for PC based translation software — it seems that Globalink in was the earliest — and it seems quite likely that it will be an additional feature for online machine translation sometime in the future.
The research in speech translation is beset with numerous problems, not just variability of voice input but also the nature of spoken language. By contrast with written language, spoken language is colloquial, elliptical, context-dependent, interpersonal, and primarily in the form of dialogues. Speech translation therefore represents a radical departure from traditional machine translation. Complexities of speech translation can, however, be reduced by restricting communication to relatively narrow domains — a favourite for many researchers has been business communication, booking of hotel rooms, negotiating dates of meetings, etc.
From these long-term projects no commercial systems have appeared yet. There are, however, other areas of speech translation which do have working but not yet commercial systems.
These are communication in patient-doctor and other health consultations, communication by soldiers in military operations, and communication in the tourism domain. Multilingual access to information in documentary sources articles, conferences, monographs, etc.
Information extraction or text mining has had similar close historical links to machine translation, strengthened likewise by the growing statistical orientation of machine translation. Many commercial and government-funded international and national organisations have to scrutinize foreign-language documents for information relevant to their activities from commercial and economic to surveillance, intelligence, and espionage.
Searching can focus on single texts or multilingual collections of texts, or range over selected databases e. These activities have also, until recently, been performed by human analysts. Now at least drafts can be obtained by statistical means — methods for summarisation have been researched since the s. The development of working systems that combine machine translation and summarisation is apparently still something for the future.
The aim is to retrieve answers in text form from databases in response to natural-language questions. Like summarization, this is a difficult task; but the possibility of multilingual question-answering is attracting more attention in recent years. Chapter 3 Creation of Parallel Corpus 3. The corpus creation for Indian languages will also be discussed elaborately. McEnrey and Wilson talk in detail about corpus linguistics. However, that does not mean that the term "corpus linguistics" was used in texts and studies from this era.
Corpus was used to study language acquisition, spelling conventions and language pedagogy. The present day interpretation of corpus is different from the earlier one. In the present era, corpus in electronic form is made use of for various purposes including NLP. Computer comes in handy to manipulate the electronic corpus. But before the advent of computer non-electronic corpuses in the hand written form were widely in use.
Such non-electronic corpuses were made use of for the following tasks Dash Corpus in dictionary making, Corpus in dialects study, Corpus for lexical study, Corpus for writing grammars, Corpus in speech study, Corpus in language pedagogy, Corpus in language acquisition and Corpus in other fields of Linguistics 3. Indeed, individual texts are often used for many kinds of literary and linguistic analysis - the stylistic analysis of a poem, or a conversation analysis of a TV talk show.
However, the notion of a corpus as the basis for a form of empirical linguistics is different from the examination of single texts in several fundamental ways. Corpus linguistics is a method of carrying out linguistic analyses using huge corpuses or collections of data. As it can be used for the investigation of many kinds of linguistic questions and as it has been shown to have the potential to yield highly interesting, fundamental, and often surprising new insights about language, it has become one of the most wide-spread methods of linguistic investigation in recent years.
In principle, corpus linguistics is an approach that aims to investigate linguistic phenomena through large collections of machine-readable texts. This approach is used within a number of research areas: In principle, any collection of more than one text can be called a corpus, corpus being Latin for "body", hence a corpus is any body of text. But the term "corpus" when used in the context of modern linguistics tends most frequently to have more specific connotations than this simple definition.
Sampling and Representativeness 2. Finite Size 3. Machine Readable Form 4. A Standard Reference 3. In such cases we have two options for data collection: We could analyse every single utterance in that variety - however, this option is impracticable except in a few cases, for example with a dead language which only has a few texts.
Usually, however, analysing every utterance would be an unending and impossible task. We could construct a smaller sample of that variety. This is a more realistic option. One of Chomsky's criticisms of the corpus approach was that language is infinite - therefore, any corpus would be skewed.
In other words, some utterances would be excluded because they are rare, others which are much more common might be excluded by chance, and alternatively, extremely rare utterances might also be included several times. Although nowadays modern computer technology allows us to collect much larger corpora than those that Chomsky was thinking about, his criticisms still must be taken seriously.
This does not mean that we should abandon corpus linguistics, but instead try to establish ways in which a much less biased and representative corpus may be constructed. We are therefore interested in creating a corpus which is maximally representative of the variety under examination, that is, which provides us with an as accurate a picture as possible of the tendencies of that variety, as well as their proportions.
This "collection of texts" as Sinclair's team prefers to call them, is an open-ended entity - texts are constantly being added to it, so it gets bigger and bigger. Monitor corpora are of interest to lexicographers who can trawl a stream of new texts looking for the occurence of new words, or for changing meanings of old words.
Their main advantages are: Their main disadvantage is: With the exception of monitor corpora, it should be noted that it is more often the case that a corpus consists of a finite number of words.
Usually this figure is determined at the beginning of a corpus-building project. For example, the Brown Corpus contains 1,, running words of text.
Unlike the monitor corpus, when a corpus reaches its grand total of words, collection stops and the corpus is not increased in size. An exception is the London-Lund corpus, which was increased in the mids to cover a wider variety of genres.
This was not always the case as in the past the word "corpus" was only used in reference to printed text. The term corpus is almost synonymous with the term machine-readable corpus. Interest in the computer for the corpus linguist comes from the ability of the computer to carry out various processes, which when required of humans, ensured that they could only be described as psuedo- techniques. The type of analysis that Kading waited years for can now be achieved in a few moments on a desktop computer.
Today few corpora are available in book form - one which does exist in this way is "A Corpus of English Conversation" Svartvik and Quirk which represents the "original" London-Lund corpus.
Corpus data not excluding context- free frequency lists is occasionally available in other forms of media. Machine-readable corpora possess the following advantages over written or spoken formats: This is something which we covered at the end of Part One.