Building a Malay Word Predictor with LSTM

white and black typewriter with white printer paper

Language models have revolutionized natural language processing tasks, including machine translation and sentiment analysis. Did you know that we have used artificial intelligence in DewanEja long before ChatGPT became a thing?

Long short-term memory (LSTM) networks, a type of recurrent neural network (RNN), are widely used for language modeling. LSTM addresses the vanishing gradient problem and excels at modeling long-range dependencies in sequential data. Its ability to retain and update information over extended periods makes it ideal for tasks involving context and sequential patterns.

Collecting a substantial amount of Malay text data from various sources is crucial. Although the Internet provide voluminous content, the challenge is to identify and extract good quality data, because a model is only as good as the training dataset. We are fortunate to have an advantage in this aspect from our years of working with Malay text.

After that, preprocessing steps such as tokenization, lowercasing, and removing punctuation and special characters are performed. We also need to handle specific challenges in Malay, such as word segmentation and handling compound words.

Building a Malay word predictor with LSTM is a challenging task that yields significant rewards. Language modeling enables intelligent systems that assist with typing, text completion, and machine translation. By addressing challenges like data scarcity and OOV words, advancements in preprocessing techniques and architectures will enhance the accuracy and robustness of Malay word prediction systems.

The end result is AutoPrediction in DewanEja 11. After typing a word, it predicts the next word for you, boosting typing speed and your productivity. Download the free trial now.