Abstract
Long short term memory (LSTM) networks have been gaining popularity in modeling sequential data such as phoneme recognition, speech translation, language modeling, speech synthesis, chatbot-like dialog systems, and others. This paper investigates the attention-based encoder-decoder LSTM networks in TIFINAGH part-of-speech (POS) tagging when it is compared to Conditional Random Fields (CRF) and Decision Tree. The attractiveness of LSTM networks is its strength in modeling long-distance dependencies. The experiment results show that Long short-term memory (LSTM) networks perform better than CRF and Decision Tree that have a near performance.
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Maarouf, O., El Ayachi, R. (2021). Part-of-Speech Tagging Using Long Short Term Memory (LSTM): Amazigh Text Written in Tifinaghe Characters. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2021. Lecture Notes in Business Information Processing, vol 416. Springer, Cham. https://doi.org/10.1007/978-3-030-76508-8_1
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