A hybrid approach in analyzing filipino morphology

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ID: 285509
2021
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Abstract
This paper presents a hybrid-approach on Filipino Morphological Analysis by combining root word extraction and a rule-based grammatical information extrac- tion. In the hopes of the machine to learn and understand the different Filipino Morphological phenomena, this work introduced and compared multiple neural network models and its variants: Feedforward Neural Networks (FFNN), Recur- rent Neural Networks (RNN), and BERT, for extracting the root word of any given Tagalog word, the performance of each models in this work was measured with accuracy and Levenshtein distance metrics and have seen that the BERT model was the best performing model on both the default test dataset (87.45%) and the UD-Treebanks dataset (79.11%). It was further compared with the MAGTaga- log Morphological analyser, which only performed 51.26% in the UD-Treebanks dataset. Although, it could be noticed that the BERT model had the biggest memory requirement and time-to-train making it slightly inefficient in terms of rapid development. Further problems regarding the models’ performance include suffixation, particularly suffixes ending ’g’ and ’ng’. It is noted that the mentioned suffixes were not also part of the official Tagalog morphological rules. It can be solved on future iterations of this work. All the proposed solutions performed very well in identifyin the different Filipino morphological phenomena and this work can be used on other NLP tasks with its API design. Keywords: Morphological Analysis
Reference Key
persistent_1760655815_68f179c7619b5 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yambao, Arian N.
Journal Malay Journal
Year 2021
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdm_comsci/1
Keywords Keywords not found

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