Microsoft Technology Licensing, LLC

Arumugam Kumaran

Joseph M Joy

Lucretia Henrica Vanderwende

Sean Blagsvedt

Tanuja Abhay Joshi

Saravanan Krishnan

Vibhuti Singh Sengar

Raghavendra Udupa U

Udayan Khurana

Tobias W M Kellner

A document in a first language and an additional document in a second language may be reviewed. It may be determined if the additional document is sufficiently similar to the document. If the additional document is determined sufficiently similar to the document, a named entity in the document may be selected. The method may search for a similar named entity by comparing the named entity to a word in the additional document and determining if the named entity and word are sufficiently similar. If a similar word to the named entity is located, the named entity and the similar named entities may be stored as name entity transliterations.

Named entity transliteration using comparable CORPRA

A cross-lingual location search uses a combination of translation and transliteration of query tokens to develop a set of candidate matches for further searching. A query is broken up into individual tokens (e.g. address parts) and a list of transliterations and/or translations for each token is developed. The translated and transliterated results are keyed against a spatial database using both literal database keys and transliterated database keys. Matches from the resulting searches are selected when a spatial overlap, or constraint, occurs among subsequences of the query tokens.

Cross lingual location search

A document translation system is described. In various embodiments, the document translation system improves translations of text or speech ('documents') that are performed by computing devices ('machine translations'). Upon translating a document into a target language, the system can provide a user interface containing both the original document and a translated document. The translated document can include portions that are translated automatically by a machine translation service, and other portions that have been amended or modified by users. Upon providing the translated document, the system may receive input from a user describing portions of the translated document that should be modified. These modifications may then be stored in corpora that the document translation system can employ during future requests to translate the document. When multiple modifications are stored, the system may select one of the stored modifications during translation, such as based on the document type, reputation of the user who provided the modification, and so forth. The stored corpora can then be employed to improve future machine translations.

Document translation system

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Bengaluru, India

Arumugam Kumaran