We are not able to deal with a mammoth text corpus without summarizing them into a
relatively small subset. A computational tool is extremely needed to understand such a …

Continuous space word embeddings learned from large, unstructured corpora have been
shown to be effective at capturing semantic regularities in language. In this paper we …

Probabilistic topic models are widely used to discover latent topics in document collections,
while latent feature vector representations of words have been used to obtain high …

Today's social platforms, such as Twitter and Facebook, continuously generate massive
volumes of data. The resulting data streams exceed any reasonable limit for permanent …

We propose a rolling version of the Latent Dirichlet Allocation, called RollingLDA. By a
sequential approach, it enables the construction of LDA-based time series of topics that are …

In this paper, we study a new problem of continuous learning from doubly-streaming data
where both data volume and feature space increase over time. We refer to the doubly …

With the emerging of new data collection ways, the features are incremental and
accumulated gradually. Due to the expansion of feature spaces, it is more common that …

Natural language processing (NLP) went through a profound transformation in the mid-
1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze …

There is a great need for technologies that can predict the mortality of patients in intensive
care units with both high accuracy and accountability. We present joint end-to-end neural …

The idea of combining the active query strategy and the passive-aggressive (PA) update
strategy in online learning can be credited to the PA active (PAA) algorithm, which has …