![]() ![]() We then develop ways of jointly learning energy functions and inference networks using an adversarial learning framework. Then, we propose a method in which we train a neural network to do argmax inference under a structured energy function, referring to the trained networks as "inference networks" or "energy-based inference networks". In this dissertation, we discuss the concept of the energy function and structured models with different energy functions. In NLP and other applications, an energy function is comparable to the concept of a scoring function. The dissertation begins with a general introduction to energy-based models. We provide a learning framework for complicated structured models as well as an inference method with a better speed/accuracy/search error trade-off. We concentrate on complex structured models in this dissertation. The structure components of their method, on the other hand, are usually relatively simple. Deep representation learning has become increasingly popular in recent years. These difficulties lead researchers to focus more on models with simple structure components (e.g., local classifier). The complex models of structured application come at the difficulty of learning and inference. ![]() Structured prediction in natural language processing (NLP) has a long history. Furthermore, the prediction accuracy and POS tagging results show that this research outperformed a comparable previous study, indicating that the Malay POS tagger model and its POS were successfully improved. The evaluation and analysis of the developed Malay POS tagger model show that the SVM classifier, as well as the newly proposed Malay POS tags, is the best machine learning algorithm for Malay Twitter data. The data was fed into machine learning algorithms after several stages of processing to serve as the training and testing corpus. This study's data was gathered by using Twitter's Advanced Search function and relevant and related keywords associated with informal Malay. For instance, Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), and K-Nearest Neighbor (KNN) classifiers. ![]() The goal of this paper is to improve existing Malay POS tags so that they are more compatible with the newly created Malay Twitter corpus, as well as to build a POS tagging model specifically tailored for Malay Twitter data using various machine learning algorithms. This paper explains why and how this study chose to create a new Malay Twitter corpus, Malay Part-of-Speech (POS) tags, and a Malay POS tagger model. Because of the popularity of Twitter, most Malaysians use it daily, providing researchers and developers with a wealth of data on Malaysian users. Almost everything that happens in a single day is tweeted by users. Twitter is a popular social media platform in Malaysia that allows for 280-character microblogging. ![]()
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