Learning Topics using Semantic Locality
This work addresses the problem of generating more meaningful topics in text documents for researchers and practitioners in NLP, though it appears incremental as it focuses on preprocessing enhancements.
The paper tackled improving topic modeling accuracy by proposing a new feature extraction technique for data preprocessing, which increased topic accuracy by up to 12.99% compared to state-of-the-art models like LDA and RBMs.
The topic modeling discovers the latent topic probability of the given text documents. To generate the more meaningful topic that better represents the given document, we proposed a new feature extraction technique which can be used in the data preprocessing stage. The method consists of three steps. First, it generates the word/word-pair from every single document. Second, it applies a two-way TF-IDF algorithm to word/word-pair for semantic filtering. Third, it uses the K-means algorithm to merge the word pairs that have the similar semantic meaning. Experiments are carried out on the Open Movie Database (OMDb), Reuters Dataset and 20NewsGroup Dataset. The mean Average Precision score is used as the evaluation metric. Comparing our results with other state-of-the-art topic models, such as Latent Dirichlet allocation and traditional Restricted Boltzmann Machines. Our proposed data preprocessing can improve the generated topic accuracy by up to 12.99\%.