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.
The morphological syntax of the Swahili verb comprises 10 slots. In this work, we present SwaRegex, a novel rule-based model for the morphological segmentation of Swahili verbs. This model is designed as a lexical transducer, which accepts a verb as an input string and outputs the morphological slot occupied by the morphemes in the input string. SwaRegex is based on regular expressions developed using the C# programming language. To test the model, we designed a web scraper that obtained verbs from an online Swahili dictionary. The scrapper separated the corpus into two datasets: dataset A, comprising 163 verbs Bantu origin; and dataset B, containing the entire set of 715 non-Arabic verb entries obtained by the web scrapper. The performance of the model was tested against a similar model designed using the Xerox Finite State Tools (XFST). The regular expressions used in both models were the same. SwaRegex outperformed the XFST model on both datasets, achieving a 98.77% accuracy on dataset A, better than the XFST model by 41.1%, and a 68.67% accuracy on dataset B, better than the XFST model by 38.46%. This work is beneficial to prospective learners of Swahili, by helping them understand the syntax of Swahili verbs, and is an integral teaching aid for Swahili. Search engines will benefit from the lexical transducer by leveraging its finite state network when lemmatizing search terms. This work will also create more opportunities for more research to be done on Swahili.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.