Machine Translation: From Real Users to Research: 6th by Jeffrey Allen (auth.), Robert E. Frederking, Kathryn B.

By Jeffrey Allen (auth.), Robert E. Frederking, Kathryn B. Taylor (eds.)

The earlier convention during this sequence (AMTA 2002) took up the subject matter “From learn to actual Users”, and sought to discover why fresh learn on data-driven desktop translation didn’t appear to be relocating to undefined. because it became out, the ?rst advertisement items of the data-driven examine move have been simply over the horizon, andintheinterveningtwoyearstheyhavebeguntoappearinthemarketplace. Atthesame time,rule-basedmachinetranslationsystemsareintroducingdata-driventechniquesinto the combo of their items. computing device translation as a software program software has a 50-year heritage. There are a growing number of intriguing deployments of MT, a lot of with a purpose to be exhibited and mentioned on the convention. however the scale of business use hasn't ever approached the estimates of the latent call for. In gentle of this, we reversed the query from AMTA 2002, to examine the next move within the route to advertisement good fortune for MT. We took person wishes as our subject, and explored how or even if industry standards are feeding into learn courses. The transition of study discoveries to functional use includes te- nicalquestionsthatarenotassexyasthosethathavedriventheresearchcommunityand examine investment. very important product matters comparable to process customizability, computing source standards, and usefulness and ?tness for specific initiatives have to have interaction the creativeenergiesofallpartsofourcommunity,especiallyresearch,aswemovemachine translation from a distinct segment software to a extra pervasive language conversion approach. Thesetopicswereaddressedattheconferencethroughthepaperscontainedinthesep- ceedings, or even extra speci?cally via a number of invited shows and panels.

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Extra resources for Machine Translation: From Real Users to Research: 6th Conference of the Association for Machine Translation in the Americas, AMTA 2004, Washington, DC, USA, September 28 - October 2, 2004. Proceedings

Example text

For example, we find serological or serologic and immunological or immunologic Other variant orthographic forms are for example onchocercosis and onchocerciasis (which of the English words is preferable is still a matter of debate between specialists in English speaking countries and specialists in non-English speaking countries); lymphopaenia and lymphopenia leukopaenia and leukopenia (British English and American English respectively). This kind of phenomenon is mainly caused by different habits or by geographical distinctions.

In addition to inserting the EOR markers, the order of words in each training instance is reversed before adding them to the index. This allows matches to be extended from left to right even though lookups in the index can only be efficiently extended from right to left. ) To retrieve the training examples containing matching the input to be translated, iterate over the range of V corresponding to the For each instance of the follow the pointers in V until reaching an EOR, then extract the record number from the EOR and retrieve the appropriate training example from the corpus.

The different arrangements lay stress on the different information intended to be transferred. As a result of the different arrangements of the sentence constituents, the semantic contents are greatly influenced. This brings extra difficulty to the analysis of Chinese sentences without referring to semantics and even pragmatics. In our work, only the first sentence structure (SVO as in example 9a)) will be recommended. These examples show in fact only a very small part of the different linguistic competence in both languages.

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