Language Independent and Multilingual Language Identification using Infinity Ngram Approach

Abstract

Now days it is possible to get massive amount of multilingual digital information that are generated, propagated, exchanged, stored and accessed through the web each day across the world. Such accumulation of multilingual digital data becomes an obstacle for information acquisition. In order to tackling such difficulty language identification is the first step among many steps that are used for information acquisition. Language identification is the process of labeling given text content into corresponding language category. In past decades research works have been done in the area of language identification. However, there are issues which are not solved until: multilingual language identification, discriminating language category of very closely related languages documents and labelling the language category for very short texts like words or phrases. In this investigation, we propose an approach which able to eradicate unsolved issues of language identification (i.e. multilingual and very short texts language identification) without language barrier. In order to attain this we adopt an approach of that uses all character ngram features of given text unit (i.e. word, phrase or etc). Moreover, the proposed approach has a capability of identify the language of a text at any text unit (i.e. word, phrase, sentence or document) in both monolingual and multilingual setting. The reason behind this capability of proposed approach is due to adopting word level features, in which every words need to be classify with regard to its language category. The infinity ngram approach uses all character ngrams of text unit together in order to label the language category of given text per word level. In order to observe the effectiveness of the proposed approach four experimental techniques are conducted: pure infinity character ngram, infinity ngram with location feature and infinity ngram with sentence and document level reformulation. The experimental result indicates that an infinity ngram with location feature and along with sentence and document level reformulation achieves a promising result, which is an average F-measure of 100% at word, phrase, sentence, document level in monolingual setting. As well, for multilingual setting also attains an average F-measure of 100% for both sentence and document level, but for phrase level achieves 84.33%, 88.95% and 90.19% For Amharic, Geeze and Tigrigna respectively. Beside this, at word level achieves 83.16%, 80.96% and 85.85% for Amharic, Geeze, and Tigrigna respectively.