Part-of-speech Tagset and Corpus Development for Igbo, an African Language

Onyenwe, Ikechukwu; Uchechukwu, Chinedu; Hepple, Mark

doi:10.3115/v1/w14-4914

Cited by 11 publications

(9 citation statements)

References 4 publications

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“…Igbo Tagset tags description and usage. See [21], [22], [23] for the full description of Igbo tagset.…”

Section: Discussionmentioning

confidence: 99%

“…New World Translation (NWT) Bible [17] provides an ideal test case for our study because of the existence of publically accessible texts of English and their translations in electronic format available in Igbo that is already POS annotated in [23]. The former allows us to use existing English POS tagger on the English texts and transfer POS tags via alignment and projection onto Igbo texts; the latter allows us to evaluate the tagged corpus on sizeable human-annotated tags.…”

Section: Datamentioning

confidence: 99%

See 1 more Smart Citation

Bootstrapping Method for Developing Part-Of-Speech Tagged Corpus in Low Resource Languages Tagset- A Focus on an African IGBO

E¹,

G²,

E³

et al. 2019

IJNLC

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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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“…Igbo Tagset tags description and usage. See [21], [22], [23] for the full description of Igbo tagset.…”

Section: Discussionmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Bootstrapping Method for Developing Part-Of-Speech Tagged Corpus in Low Resource Languages Tagset- A Focus on an African IGBO

E¹,

G²,

E³

et al. 2019

IJNLC

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“…The preprocessing task relied substantially on the approaches used by Onyenwe et al (2014). Observed language based patterns were preserved.…”

Section: Preprocessingmentioning

confidence: 99%

Lexical Disambiguation of Igbo using Diacritic Restoration

Ezeani¹,

Hepple²,

Onyenwe³

2017

Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and Their Applications

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Properly written texts in Igbo, a low resource African language, are rich in both orthographic and tonal diacritics. Diacritics are essential in capturing the distinctions in pronunciation and meaning of words, as well as in lexical disambiguation. Unfortunately, most electronic texts in diacritic languages are written without diacritics. This makes diacritic restoration a necessary step in corpus building and language processing tasks for languages with diacritics. In our previous work, we built some n−gram models with simple smoothing techniques based on a closedworld assumption. However, as a classification task, diacritic restoration is well suited for and will be more generalisable with machine learning. This paper, therefore, presents a more standard approach to dealing with the task which involves the application of machine learning algorithms.

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“…The 28 consonant characters are "b, ch, d, f, g, gb, gh, gw, h, j, k, kw, kp, l, m, n, nw, ny, ṅ, p, r, s, sh, t, v, w, y, z" and 8 vowels characters are "a, e, i, ị, o, ọ, u, ụ". There are nine consonants characters that are digraphs: "ch, gb, gh, gw, kp, kw, nw, ny, sh" [6]. It uses a Roman Script and it is a tonal language with two distinct tones, high and low.…”

Section: Igbo Languagementioning

confidence: 99%

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

J¹,

Ugwu²,

Adegbola³

2017

IJDMTA

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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.

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Part-of-speech Tagset and Corpus Development for Igbo, an African Language

Cited by 11 publications

References 4 publications

Bootstrapping Method for Developing Part-Of-Speech Tagged Corpus in Low Resource Languages Tagset- A Focus on an African IGBO

Bootstrapping Method for Developing Part-Of-Speech Tagged Corpus in Low Resource Languages Tagset- A Focus on an African IGBO

Lexical Disambiguation of Igbo using Diacritic Restoration

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

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