engleski

An economic approach to big data in a minority language

Google's n-gram project brought recently big data benefits to several main world languages, like English, Chinese etc. Any attempt to derive such systems, aimed to accelerate the development of NLP applications for world minority languages, in the manner in which it has been done in the project, encounters many obstacles. This paper presents an innovative and economic approach to large-scale n-gram system creation applied to the Croatian language case. Instead of using the Web as the world's biggest text repository, our process of n-gram collection relies on the Croatian academic online spellchecker Hascheck, a language service publicly available since 1993 and popular worldwide. The service has already processed a corpus whose size exceeds the size of the Croatian web-corpus created in recent years. Contrary to the Google n-gram systems, where cutoff criteria were applied, our n-gram filtering is based on dictionary criteria. This resulted in a system comparable in size to the largest n-gram systems of today. Because of the reliance on a service in constant use, the Croatian n-gram system is a dynamic one, unique among the systems compared. The importance of having an n-gram infrastructure for rapid breakthroughs in new application areas is also exemplified in the paper.

Croatian language; language modeling; lexical n - gram; n - gram count comparison; traffic modeling .

nije evidentirano