Morphological Segmentation with LSTM Neural Networks for Tigrinya

Tedla, Yemane; Yamamoto, Kazumasa

doi:10.5121/ijnlc.2018.7203

Cited by 5 publications

(8 citation statements)

References 14 publications

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“…In their novel morphological segmentation work for Tigrinya language, (Tedla & Yamamoto, 2018) combine CRFs with LSTMS for detecting morpheme boundaries. Begin (B), Inside (I), Outside (O), Single (S) and End (E) labels are used to annotate morphemes in order to mark morpheme boundaries.…”

Section: Machine Learningmentioning

confidence: 99%

review of techniques for morphological analysis in natural language processing

Muthee

Makau²,

Amos³

2022

AJSTSS

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Natural language is a crucial tool to facilitate communication in our day-to-day activities. This can be achieved either in text or speech forms. Natural language processing (NLP) involves making computers understand and process natural language. NLP has enhanced the way humans interact with computers, from having computers use speech to talk to humans as well as having computers translate human speech. Apart from speech, computers also create and understand sentences in natural language in a process called morphological analysis. Morphological analysis is an important part in natural language processing, being applied as a preprocessing step in most NLP tasks. Morphological analysis consists of four subtasks, that is, lemmatization, part-of-speech (POS) tagging, word segmentation and stemming. In this paper, we explore in detail each of these tasks of morphological analysis. We then evaluate the techniques used in this NLP field. Finally, we give a summary of the results of each of these techniques.

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Section: Machine Learningmentioning

confidence: 99%

review of techniques for morphological analysis in natural language processing

Muthee

Makau²,

Amos³

2022

AJSTSS

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“…Tigrinya is a language commonly used in Eritrea and Ethiopia, with more than 7 million speakers worldwide [16]. It is one of the Semitic languages with the likes of Amharic, Arabic, and Hebrew [17].…”

Section: Tigrinyamentioning

confidence: 99%

“…Tigrinya alphabet has 35 base letters with 7 vowels and some extra letters formed by variation of those base letters with 5 vowels to form a list of around 275 unique symbols [20]. By default, Tigrinya has subject-object-verb (SOV) Figure 1: Transfer a monolingual transformer model to new target language word order, although this is not always the case with some exception [16,21]. The writing system is from left to right with each word is separated by a space; however, Tigrinya is a morphologically rich language where multiple morphemes can be packed together in a single word.…”

Section: Tigrinyamentioning

confidence: 99%

“…The morphology of Tigrinya is similar to Semitic languages, including Amharic and Arabic, which have a root-andpattern type [16]. Moreover, Tigrinya has a complex morphological structure with many variations of a given root verb not only by its prefix and suffix but also its internal inflections of the root-and-pattern type [16,18].…”

Section: Tigrinyamentioning

confidence: 99%

“…Tedla and Yamamoto [16] studied a detailed morphological segmentation of Tigrinya language. The authors chose to use Latin transliterations of the given Geez scripts due to the syllabic properties of Tigrinyas letters, which can result in alterations of characters in the segmentation boundaries.…”

Section: Tigrinyamentioning

confidence: 99%

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Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya

Tela,

Woubie,

Hautamaki

2020

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In recent years, transformer models have achieved great success in natural language processing (NLP) tasks. Most of the current state-of-the-art NLP results are achieved by using monolingual transformer models, where the model is pretrained using a single language unlabelled text corpus. Then, the model is fine-tuned to the specific downstream task. However, the cost of pre-training a new transformer model is high for most languages. In this work, we propose a cost-effective transfer learning method to adopt a strong source language model, trained from a large monolingual corpus to a low-resource language. Thus, using XLNet language model, we demonstrate competitive performance with mBERT and a pre-trained target language model on the cross-lingual sentiment (CLS) dataset and on a new sentiment analysis dataset for low-resourced language Tigrinya. With only 10k examples of the given Tigrinya sentiment analysis dataset, English XLNet has achieved 78.88% F1-Score outperforming BERT and mBERT by 10% and 7%, respectively. More interestingly, fine-tuning (English) XLNet model on the CLS dataset has promising results compared to mBERT and even outperformed mBERT for one dataset of the Japanese language.

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Hybrid Morph-Analysis Model for Marathi

Divate

2024

Lecture Notes in Networks and Systems

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Morphological Segmentation with LSTM Neural Networks for Tigrinya

Abstract: ABSTRACT

Cited by 5 publications

References 14 publications

review of techniques for morphological analysis in natural language processing

review of techniques for morphological analysis in natural language processing

Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya

Hybrid Morph-Analysis Model for Marathi

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