Named entity translation

#The present application describes systems and techniques relating to machine translation of languages, for example, translation of named entities.

#Named entity phrases are being introduced in news stories on a daily basis in the form of personal names, organizations, locations, temporal phrases, and monetary expressions. Various techniques to identify named entities have been made available.

#The present disclosure includes systems and techniques relating to translating named entities from a source language to a target language. According to an aspect, potential translations of a named entity from a source language to a target language are generated using a pronunciation-based and spelling-based transliteration model. A monolingual resource in the target language can be searched for information relating to usage frequency, and output including at least one of the potential translations can be provided based on the usage frequency information.

#FIG. 1 illustrates a system using named entity translation.

#FIG. 2 illustrates an exemplary named entity translation system.

#FIG. 3 illustrates machine-based production of potential translations for a named entity.

#Details of one or more embodiments are set forth in the accompanying drawings and the description below. Other features and advantages may be apparent from the description and drawings, and from the claims.

#As used herein, the terms “named entity”, “named entity phrase” and “phrase” are used interchangeably. A named entity is a group of one or more words that identifies an entity by name. For example, named entities may include persons, organizations, locations, dates, and monetary expressions. Additionally, the terms “electronic document” and “document” mean a set of electronic data, including both electronic data stored in a file and electronic data received over a network. An electronic document does not necessarily correspond to a file. A document may be stored in a portion of a file that holds other documents, in a single file dedicated to the document in question, or in a set of coordinated files. The term “text” means character data, which may be encoded using a standardized character encoding scheme, such as Unicode, ASCII, Arabic (ISO), Turkish (ISO), Chinese Simplified (EUC), Korean (ISO), etc.

#FIG. 1 illustrates a system 100 using named entity translation. The system 100 includes a natural language processing (NLP) system 120, such as a statistical machine translation system, a Cross-Lingual Information Retrieval (CLIR) system or a Question Answering (QA) system. The NLP system 120 can have a built-in named entity translation system 110. Alternatively, the NLP system 120 can be in communication with the named entity translation system 110, such as by through a network 130.