Topic modeling is one of the most powerful techniques in text mining for data mining, latent
data discovery, and finding relationships among data and text documents. Researchers …

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine
learning paradigm that continuously learns by accumulating past knowledge that it then …

How can a single person understand what's going on in a collection of millions of
documents? This is an increasingly common problem: sifting through an organization's e …

Language models are generally trained on data spanning a wide range of topics (eg, news,
reviews, fiction), but they might be applied to an a priori unknown target distribution (eg …

Topic models are statistical models for learning the latent structure in document collections,
and have gained much attention in the machine learning community over the last decade …

Customer reviews on the Internet reflect users' sentiments about the product, service, and
social events. As sentiments can be divided into positive, negative, and neutral forms …

When building machine translation systems, one often needs to make the best out of
heterogeneous sets of parallel data in training, and to robustly handle inputs from …

Semi-parametric models, which augment generation with retrieval, have led to impressive
results in language modeling and machine translation, due to their ability to retrieve fine …

In this paper, we propose a new domain adaptation technique for neural machine translation
called cost weighting, which is appropriate for adaptation scenarios in which a small in …

Traditional neural machine translation (NMT) methods use the word-level context to predict
target language translation while neglecting the sentence-level context, which has been …