Analysis and Representation of Igbo Text Document for a Text-Based System

Abstract

The advancement in Information Technology (IT) has assisted in inculcating the three Nigeria major languages in text-based application such as text mining, information retrieval and natural language processing. The interest of this paper is the Igbo language, which uses compounding as a common type of word formation and as well has many vocabularies of compound words. The issues of collocation, word ordering and compounding play high role in Igbo language. The ambiguity in dealing with these compound words has made the representation of Igbo language text document very difficult because this cannot be addressed using the most common and standard approach of the Bag-Of-Words (BOW) model of text representation, which ignores the word order and relation. However, this cause for a concern and the need to develop an improved model to capture this situation. This paper presents the analysis of Igbo language text document, considering its compounding nature and describes its representation with the Word-based N-gram model to properly prepare it for any text-based application. The result shows that Bigram and Trigram n-gram text representation models provide more semantic information as well addresses the issues of compounding, word ordering and collocations which are the major language peculiarities in Igbo. They are likely to give better performance when used in any Igbo textbased system.

Development and Comparison of Multiple Emotion Classification Models in Indonesia Text Using Machine Learning

“…Moreover, TF-IDF, as indicated by several studies, has been shown to improve accuracy compared to other feature extraction methods when applied to machine learning algorithms [36,39]. Furthermore, BoW is chosen because it represents words based on their frequency of occurrence within a sentence in textual data [40].…”