Corpus Generation to Develop Amharic Morphological Segmenter

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AXIX2EJI7" target="_blank" >RIV/00216208:11320/23:XIX2EJI7 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.proquest.com/docview/2883174197/abstract/B90879C438B4510PQ/1" target="_blank" >https://www.proquest.com/docview/2883174197/abstract/B90879C438B4510PQ/1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14569/IJACSA.2023.01409116" target="_blank" >10.14569/IJACSA.2023.01409116</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Corpus Generation to Develop Amharic Morphological Segmenter

Popis výsledku v původním jazyce

"Morphological segmenter is an important component in Amharic natural language processing systems. Despite this fact, Amharic lacks large amount of morphologically segmented corpus. Large amount of corpus is often a requirement to develop neural network-based language technologies. This paper presents an alternative method to generate large amount of morph-segmented corpus for Amharic language. First, a relatively small (138,400 words) morphologically annotated Amharic seed-corpus is manually prepared. The annotation enables to identify prefixes, stem, and suffixes of a given word. Second, a supervised approach is used to create a conditional random field-based seed-model (on the seed-corpus). Applying the seed-model (an unsupervised technique on a large unsegmented raw Amharic words) for prediction, a large corpus size (3,777,283) of segmented words are automatically generated. Third, the newly generated corpus is used to train an Amharic morphological segmenter (based on a supervised neural sequence-to-sequence (seq2seq) approach using character embeddings). Using the seq2seq method, an F-score of 98.65% was measured. Results show an agreement with previous efforts for Arabic language. The work presented here has profound implications for future studies of Ethiopian language technologies and may one day help solve the problem of the digital-divide between resource-rich and under-resourced languages."

Název v anglickém jazyce

Corpus Generation to Develop Amharic Morphological Segmenter

Popis výsledku anglicky

"Morphological segmenter is an important component in Amharic natural language processing systems. Despite this fact, Amharic lacks large amount of morphologically segmented corpus. Large amount of corpus is often a requirement to develop neural network-based language technologies. This paper presents an alternative method to generate large amount of morph-segmented corpus for Amharic language. First, a relatively small (138,400 words) morphologically annotated Amharic seed-corpus is manually prepared. The annotation enables to identify prefixes, stem, and suffixes of a given word. Second, a supervised approach is used to create a conditional random field-based seed-model (on the seed-corpus). Applying the seed-model (an unsupervised technique on a large unsegmented raw Amharic words) for prediction, a large corpus size (3,777,283) of segmented words are automatically generated. Third, the newly generated corpus is used to train an Amharic morphological segmenter (based on a supervised neural sequence-to-sequence (seq2seq) approach using character embeddings). Using the seq2seq method, an F-score of 98.65% was measured. Results show an agreement with previous efforts for Arabic language. The work presented here has profound implications for future studies of Ethiopian language technologies and may one day help solve the problem of the digital-divide between resource-rich and under-resourced languages."

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

"International Journal of Advanced Computer Science and Applications"

ISSN

2158107X

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

1114 - 1122

Kód UT WoS článku

001084849700001

EID výsledku v databázi Scopus

2-s2.0-85173165158