# Bootstrapping Method for Developing Part-of-Speech Tagged Corpus in Low Resource Languages Tagset - A Focus on an African Igbo

## TL;DR

This paper presents a bootstrapping approach combining cross-lingual and monolingual methods to develop a POS tagged corpus for low-resource languages, demonstrated on Igbo, improving annotation accuracy and efficiency.

## Contribution

It introduces a novel combined bootstrapping method leveraging parallel texts and NLP resources to create POS tagged corpora for low-resource languages like Igbo.

## Key findings

- Accuracy of POS tagging improved from 6.13% to 83.79%.
- Tags transformation rate increased from 8.67% to 98.37%.
- Method effectively accelerates POS corpus development for low-resource languages.

## Abstract

Most languages, especially in Africa, have fewer or no established part-of-speech (POS) tagged corpus. However, POS tagged corpus is essential for natural language processing (NLP) to support advanced researches such as machine translation, speech recognition, etc. Even in cases where there is no POS tagged corpus, there are some languages for which parallel texts are available online. The task of POS tagging a new language corpus with a new tagset usually face a bootstrapping problem at the initial stages of the annotation process. The unavailability of automatic taggers to help the human annotator makes the annotation process to appear infeasible to quickly produce adequate amounts of POS tagged corpus for advanced NLP research and training the taggers. In this paper, we demonstrate the efficacy of a POS annotation method that employed the services of two automatic approaches to assist POS tagged corpus creation for a novel language in NLP. The two approaches are cross-lingual and monolingual POS tags projection. We used cross-lingual to automatically create an initial 'errorful' tagged corpus for a target language via word-alignment. The resources for creating this are derived from a source language rich in NLP resources. A monolingual method is applied to clean the induce noise via an alignment process and to transform the source language tags to the target language tags. We used English and Igbo as our case study. This is possible because there are parallel texts that exist between English and Igbo, and the source language English has available NLP resources. The results of the experiment show a steady improvement in accuracy and rate of tags transformation with score ranges of 6.13% to 83.79% and 8.67% to 98.37% respectively. The rate of tags transformation evaluates the rate at which source language tags are translated to target language tags.

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Source: https://tomesphere.com/paper/1903.05225