Abstract: Methods, systems, and media are provided for identifying languages in multilingual text. A document is decoded into a universal representative coding for easier tag manipulation, then broken into plain-text content sections. The sections are identified and assigned a weight, wherein more informative sections are given a higher weight and less informative sections are given a lesser weight. A language likelihood score is determined for each word, phrase, or character n-gram in a section. The language likelihood scores within a section are combined for each language. The combined section scores are then summed together to obtain a total document score for each language. This results in a document score for each language, which can be ranked to determine the primary language for the document.

Abstract: Methods, systems, and media are provided for identifying languages in multilingual text. A document is decoded into a universal representative coding for easier tag manipulation, then broken into plain-text content sections. The sections are identified and assigned a weight, wherein more informative sections are given a higher weight and less informative sections are given a lesser weight. A language likelihood score is determined for each word, phrase, or character n-gram in a section. The language likelihood scores within a section are combined for each language. The combined section scores are then summed together to obtain a total document score for each language. This results in a document score for each language, which can be ranked to determine the primary language for the document.