Clustering short text streams is a challenging task due to its unique properties: infinite length, sparse data representation and cluster evolution.Existing approaches often exploit short text streams in a batch way.However, determine the optimal batch size is usually a difficult task since we have no prior knowledge when the topics evolve.In addition, traditional independent word representation in the graphical model tends to cause "term ambiguity" problem in short text clustering.Therefore, in this paper, we propose an Online Semantic-enhanced Dirichlet Model for short text stream clustering, called OSDM, which integrates the word-occurrence semantic information (i.e., context) into a new graphical model and clusters for each arriving short text automatically in an online way.Extensive results have demonstrated that OSDM gives better performance compared to many state-ofthe-art algorithms on both synthetic and realworld data sets.

We introduce the SiNER: a named entity recognition (NER) dataset for low-resourced Sindhi language with quality baselines. It contains 1,338 news articles and more than 1.35 million tokens collected from Kawish and Awami Awaz Sindhi newspapers using the begin-inside-outside (BIO) tagging scheme. The proposed dataset is likely to be a significant resource for statistical Sindhi language processing. The ultimate goal of developing SiNER is to present a gold-standard dataset for Sindhi NER along with quality baselines. We implement several baseline approaches of conditional random field (CRF) and recent popular state-of-the-art bi-directional long-short term memory (Bi-LSTM) models. The promising F1-score of 89.16 outputted by the Bi-LSTM-CRF model with character-level representations demonstrates the quality of our proposed SiNER dataset.