Learning syllables using CONV-LSTM model for Swahili word representation and part-of-speech tagging

Show simple item record

dc.contributor.author Shikali, Casper S.
dc.contributor.author Mokhosi, Refuoe
dc.contributor.author Shijie, Zhou
dc.contributor.author Qihe, Liu
dc.date.accessioned 2021-06-28T08:37:30Z
dc.date.available 2021-06-28T08:37:30Z
dc.date.issued 2021-06-04
dc.identifier.citation ACM Transactions on Asian and Low-Resource Language Information Processing Volume 20 Issue 4 June 2021 Article No.: 58pp 1–25 en_US
dc.identifier.uri https://dl.acm.org/doi/abs/10.1145/3445975#pill-authors__contentcon
dc.identifier.uri http://repository.seku.ac.ke/handle/123456789/6278
dc.description https://doi.org/10.1145/3445975 en_US
dc.description.abstract The need to capture intra-word information in natural language processing (NLP) tasks has inspired research in learning various word representations at word, character, or morpheme levels, but little attention has been given to syllables from a syllabic alphabet. Motivated by the success of compositional models in morphological languages, we present a Convolutional-long short term memory (Conv-LSTM) model for constructing Swahili word representation vectors from syllables. The unified architecture addresses the word agglutination and polysemous nature of Swahili by extracting high-level syllable features using a convolutional neural network (CNN) and then composes quality word embeddings with a long short term memory (LSTM). The word embeddings are then validated using a syllable-aware language model (31.267) and a part-of-speech (POS) tagging task (98.78), both yielding very competitive results to the state-of-art models in their respective domains. We further validate the language model using Xhosa and Shona, which are syllabic-based languages. The novelty of the study is in its capability to construct quality word embeddings from syllables using a hybrid model that does not use max-over-pool common in CNN and then the exploitation of these embeddings in POS tagging. Therefore, the study plays a crucial role in the processing of agglutinative and syllabic-based languages by contributing quality word embeddings from syllable embeddings, a robust Conv–LSTM model that learns syllables for not only language modeling and POS tagging, but also for other downstream NLP tasks. en_US
dc.language.iso en en_US
dc.title Learning syllables using CONV-LSTM model for Swahili word representation and part-of-speech tagging en_US
dc.type Article en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Dspace


Browse

My Account