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 …

Probabilistic topic models are unsupervised generative models which model document
content as a two-step generation process, that is, documents are observed as mixtures of …

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 …

The distributional hypothesis of Harris (1954), according to which the meaning of words is
evidenced by the contexts they occur in, has motivated several effective techniques for …

Recent advances in research tools for the systematic analysis of textual data are enabling
exciting new research throughout the social sciences. For comparative politics, scholars who …

We introduce bilingual word embeddings: semantic embeddings associated across two
languages in the context of neural language models. We propose a method to learn …

Word embeddings have been found useful for many NLP tasks, including part-of-speech
tagging, named entity recognition, and parsing. Adding multilingual context when learning …

We develop the multilingual topic model for unaligned text (MuTo), a probabilistic model of
text that is designed to analyze corpora composed of documents in two languages. From …

This paper studies the problem of question retrieval in community question answering
(CQA). To bridge lexical gaps in questions, which is regarded as the biggest challenge in …

In this paper, we develop multilingual supervised latent Dirichlet allocation (MLSLDA), a
probabilistic generative model that allows insights gleaned from one language's data to …