Encyclopedia of Computer Science and Technology

LLAN See local area network.language translation softwareAnyone who has learned a new language has also gainedan appreciation for how difficult it is to translate from onelanguage to another while preserving the intent, meaning,and context of the original. Not surprisingly, developingsoftware to perform this task, often called “machinetranslation” (MT), has also proven to be difficult. (For amore general discussion of how languages can be representedor studied using a computer, see linguistics andcomputing.)Rules-Based ApproachesThere are several approaches that can be taken to automaticlanguage translation. A rules-based system parses the originaltext to construct an intermediate representation. Theprogram then “transfers” the represented structure to anequivalent structure in the target language, drawing uponextensive lexicons (dictionaries) containing such thingsas phrase structures, word structures (morphology), andsemantics (meanings). Developing this extensive knowledgebase and the rules for manipulating it is the mostchallenging part of developing rules-based language translationsystems. (For more on the general process of computer“understanding” of language, see natural languageprocessing.)Generally a translation produced by a rules-based systemwill be intelligible to a speaker of the target language,who will be able to understand the broad meaning of theoriginal text. However, it is likely to sound “awkward” andmiss certain nuances.A simplified approach is based on a dictionary of wordsor phrases and their meanings. Each source word or phraseis simply looked up and converted to its equivalent in thetarget language. Because it does not deal with grammaticalstructure or context, this method is not very satisfactoryexcept perhaps for translating simple lists or catalogues.Statistical ApproachesThe other main approach to automatic translation relieson statistical analysis of a large body of text (corpus) thatis already translated into two languages. For example, theBayes theorem (see Bayesian analysis) can be used to estimatethe probability that string A in French (for example,“c’est un chien”) will occur in the English version as stringA’ “it’s a dog.”). Depending on the application, the sameapproach can be applied word for word, phrase for phrase,or sentence for sentence. Statistical approaches have hadgood success (particularly if the corpus is both representativeand sufficiently extensive). However, since it is basedon probability, there is always a chance that a segment oftext will be given the most likely translation rather than themeaning intended by the writer.Evaluation and ApplicationsThere are a number of features in real human languagesand usage that are challenging for translation software todeal with. Words can be ambiguous due to multiple mean-270