Evaluation of clustering techniques on Urdu News head-lines: a case of short length text

Abstract

ABSTRACT Document clustering and topic modelling methods are useful in determining groups of semantically similar documents. In literature, various studies have been conducted to evaluate the performance of lexical and semantic features on a clustering task. However, the focus of such studies was on the English language which is considered a high resource language. This paper analyses the effect of various feature extraction techniques combined with clustering and topic modelling algorithms on grouping news headlines written in Urdu, a low-resource language. Various features were first extracted using different approaches such as term-frequency inverse document frequency (TF-IDF), word embeddings learned using Word2vec and fastText models, deep contextualised word embeddings from BERT, and document embeddings extracted from the Doc2Vec model. Three different clustering algorithms: K-Means, Affinity Propagation and Density-based spatial clustering of applications with noise (DBSCAN) were then used to produce clusters for a given set of news headlines. The Latent Dirichlet Allocation topic model was also assessed for comparison with clustering algorithms. The evaluation based on extrinsic measures revealed that higher-quality clusters were produced when the K-Means algorithm was applied on a frequent unigrams-based TF-IDF feature matrix. In addition, several word embedding models have been trained on the domain of Urdu News headlines using state-of-the-art techniques such as Word2Vec, Doc2Vec, and fastText.