Machine Translation
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Machine Translation using Knowledge Based System Need of Machine Translation Issues of Machine Translation Rule Based Machine Translation
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Machine Translation
- 4. 72.1 percent of the consumers spend most or all of their time on sites in their own language 72.4 percent say they would be more likely to buy a product with information in their own language 56.2 percent say that the ability to obtain information in their own language is more important than price.
- 5. Real-time communications where it would not be practical for a human to translate (e.g. chat and email.)
- 6. Content that does not need to be perfect but just approximately understandable (e.g. any website for a quick review.) Content that would normally be too expensive or too slow to translate with a human only translation approach (e.g. many projects that have insufficient budget for a human only approach.) High value content that is changing every hour and every day there is time sensitivity (e.g. stock market news.) AFTER 1 HOUR AFTER 2 HOUR
- 7. RULE BASED MACHINE TRANSLATION (RBMT) STATISTICAL MACHINE TRANSLATION (SMT)
- 8. • Rules-based systems use a combination of language and grammar rules plus dictionaries for common words. Specialist dictionaries are created to focus on certain industries or disciplines. RULE BASED APPROACH GRAMMAR RULE LEXICON SOFTWARE PROGRAM
- 11. • Method based on Dictionary entries, which means that the words will be translated as a dictionary does – word by word, usually without much correlation of meaning between them followed by some syntactic arrangement. • Dictionary lookups may be done with or without morphological analysis(Structure of word). • Direct - based machine translation is ideally suitable for the translation of long lists of phrases. SL-TL Dictionary SL TEXT TL TEXT
- 13. SL TEXT ANALYSIS TRANSFER GENERATION TL TEXT SL DICTIONARY / GRAMMAR SL-TL DICTIONARY /GRAMMAR TL GRAMMAR/ DICTIONARY • In this translation system, a database of translation rules is used to translate text from source to target language. Whenever a sentence matches one of the rules, or examples , it is translated directly using a dictionary. • A transfer-based approach first converts the source language into an internal representation (IR) which is dependent on the source but not the Target language. The system then transform IRs into a form IRt which is independent of the source language and depends only on the Target lagunage and finally generates the target language output from IRt Lexical Level Syntactic Level Semantic Level Intermediate Representation Based On Source Language Intermediate Representation Based On Target Language
- 14. ANALYSIS TRANSFER GENERATION सीता बाग में सोयि ANALYSIS
- 15. • The Interlingual approach converts the input into a single internal representation(IR) That is independent of both source and target languages,and then converts from this into the output. Analysis Interlingua Representation Generation SL TEXT TL TEXT • The advantage in multilingual machine translations is that no transfer component has to be created for each language pair • The obvious disadvantage is that the definition of an interlingua is difficult and maybe even impossible for a wider domain.
- 16. STATISTICAL MACHINE TRANSLATION • Statistical machine translation (SMT) learns how to translate by analyzing existing human translations (known as bilingual text corpora). • Machine translator can use a database as the source for all the information it need for translating.
- 17. ISSUES IN MACHINE TRANSLATION • Word order Word order in languages differs. 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. For example, English language has SVO and Hindi language has SOV sentence structure.
- 18. • Ambiguity A given word or sentence can have more than one meaning.For ex, the word ‘’party’’ could mean a polytical party, or a social event,and deciding the suitable one in perticular case is crucial to getting right analysis and therefore right translation • The third reason is that when human use natural language, they use an enormous amount of common sense, and knowledge about the world, which helps to resolve the ambiguity. For ex. in ‘’He went to the bank, but it was closed for lunch’’,we can infer that ‘bank’ refers to a financial institution, and not a river bank, because we know from our knowledge of the world that only the former type of bank can be closed for lunch.
- 19. SYSTRAN TRANSLATOR • RULE BASED MACHINE TRANSLATION SYSTEM. • SUPPORT 45 LANGUGAES. BING TRANSLATOR • STATISTICAL BASED MACHINE TRANSLATION. • SUPPORT 47 LANGUGAES. GOOGLE TRANSLATOR • STATISTICAL BASED MACHINE TRANSLATION • SUPPORT 80 LANGUAGES. EXISTING MACHINE TRANSLATION
Hinweis der Redaktion
- If you want to read a novel written in french language then just feed that novel into machine and you will get the translated version of that novel in your language. Machine translation help to remove language barrier.
- There is lot’s of information available on internet but the same information is not available in vernacular langugage like hindi and malayalam. Taking the case of india only 3% people know english so a small set of people can get get access to these information.This phenomena is called as digital divide.
- 1. 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. The next step in direct MT system is bilingual dictionary lookup. 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. 2. Disadvantage a). Direct MT involves only lexical analysis. It does not consider structure and relationships between words. b). Direct MT systems can be quite expensive, for multilingual scenarios c). Some of the source text meaning can be lost in the translation
- 1. These components incorporate a lot of knowledge about words(Lexical Knowledge), and about the language (Linguistic Knowledge). 2. Such knowledge is stored in one or more lexicons ,and possibly other sources of linguistic knowledge ,such as grammar. 3.Analysis Phase Consist of three LEVEL Analysis Phase is used to produce source language structure. LEXICAL LEVEL This level deals with looking at the input string of characters and seperating them into tokens,which may be words,space or punctuation. This level also deal with issues like hyphenated words,and misspeltwords It is the lexical level which tells us that the input ‘’he joined the parti’’consist of four words of which the last is incorrect. This level is sometimes called ‘tokenisation’or ‘lexical analysis’. SYNTATIC LEVEL 1)This level deals with identifying the structure of a sentence,and verifying whether a sentence is grammatically correct. 2) For ex., a typical English sentence would consist of a subject and predicate.The subject is normally a noun phrase and the predicate is a verb phrase,and so on. 3) The syntactic level tells us the sentence ‘’He the party joined’’ is (syntactically) incorrect, even though each word in it is (lexically) correct. SEMANTIC LEVEL This level deals with the meaning of the input It is the semantic level which tells us that the sentence ‘’He ate the Party’’ is semantically incorrect,though it is lexically and syntactically well formed 4.Transfer phase is used to transfer source language representation to a target level representation. 5. Generation phase is used to generate target language text using target level structure Transfer-based approach has following advantages. a). It has a modular structure. b). The system easily handles ambiguities that carry over from one language to another
- In the direct approach, words are translated directly without passing through an additional representation. In the transfer approach, the source language is transformed into an abstract, less language-specific representation.