Editing Machine translation (section)

===Named entities===
{{main|Named entity}}
In [[information extraction]], named entities, in a narrow sense, refer to concrete or abstract entities in the real world such as people, organizations, companies, and places that have a proper name: George Washington, Chicago, Microsoft.  It also refers to expressions of time, space and quantity such as 1 July 2011, $500.

In the sentence "Smith is the president of Fabrionix" both ''Smith'' and ''Fabrionix'' are named entities, and can be further qualified via first name or other information; "president" is not, since Smith could have earlier held another position at Fabrionix, e.g. Vice President.
The term [[rigid designator]] is what defines these usages for analysis in statistical machine translation.

Named entities must first be identified in the text; if not, they may be erroneously translated as common nouns, which would most likely not affect the [[Bilingual evaluation understudy|BLEU]] rating of the translation but would change the text's human readability.<ref>{{Cite conference |last1=Babych |first1=Bogdan |last2=Hartley |first2=Anthony |date=2003 |title=Improving Machine Translation Quality with Automatic Named Entity Recognition |url=http://www.cl.cam.ac.uk/~ar283/eacl03/workshops03/W03-w1_eacl03babych.local.pdf |conference=Paper presented at the 7th International EAMT Workshop on MT and Other Language Technology Tools... |archive-url=https://web.archive.org/web/20060514031411/http://www.cl.cam.ac.uk/~ar283/eacl03/workshops03/W03-w1_eacl03babych.local.pdf |archive-date=14 May 2006 |access-date=4 November 2013 |url-status=dead}}</ref> They may be omitted from the output translation, which would also have implications for the text's readability and message.

[[Transliteration]] includes finding the letters in the target language that most closely correspond to the name in the source language.  This, however, has been cited as sometimes worsening the quality of translation.<ref>Hermajakob, U., Knight, K., & Hal, D. (2008).  [http://www.aclweb.org/old_anthology/P/P08/P08-1.pdf#page=433 Name Translation in Statistical Machine Translation Learning When to Transliterate] {{Webarchive|url=https://web.archive.org/web/20180104073326/http://www.aclweb.org/old_anthology/P/P08/P08-1.pdf#page=433 |date=4 January 2018 }}.  Association for Computational Linguistics.  389–397.</ref> For "Southern California" the first word should be translated directly, while the second word should be transliterated.  Machines often transliterate both because they treated them as one entity.  Words like these are hard for machine translators, even those with a transliteration component, to process.

Use of a "do-not-translate" list, which has the same end goal – transliteration as opposed to translation.<ref name="singla">{{Citation |last1=Neeraj Agrawal |title=Using Named Entity Recognition to improve Machine Translation |url=http://nlp.stanford.edu/courses/cs224n/2010/reports/singla-nirajuec.pdf |archive-url=https://web.archive.org/web/20130521075940/http://nlp.stanford.edu/courses/cs224n/2010/reports/singla-nirajuec.pdf |access-date=4 November 2013 |archive-date=21 May 2013 |last2=Ankush Singla |mode=cs1 |url-status=live}}</ref>  still relies on correct identification of named entities.

A third approach is a class-based model. Named entities are replaced with a token to represent their "class"; "Ted"  and "Erica" would both be replaced with "person" class token. Then the statistical distribution and use of person names, in general, can be analyzed instead of looking at the distributions of "Ted" and "Erica" individually, so that the probability of a given name in a specific language will not affect the assigned probability of a translation. A study by Stanford on improving this area of translation gives the examples that different probabilities will be assigned to "David is going for a walk" and "Ankit is going for a walk" for English as a target language due to the different number of occurrences for each name in the training data. A frustrating outcome of the same study by Stanford (and other attempts to improve named recognition translation) is that many times, a decrease in the [[Bilingual evaluation understudy|BLEU]] scores for translation will result from the inclusion of methods for named entity translation.<ref name="singla" />