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 …

While continuous word embeddings are gaining popularity, current models are based solely
on linear contexts. In this work, we generalize the skip-gram model with negative sampling …

Abstract Open Information Extraction (IE) is the task of extracting assertions from massive
corpora without requiring a pre-specified vocabulary. This paper shows that the output of …

Abstract Open Information Extraction (IE) systems extract relational tuples from text, without
requiring a pre-specified vocabulary, by identifying relation phrases and associated …

Tweets are the most up-to-date and inclusive stream of in-formation and commentary on
current events, but they are also fragmented and noisy, motivating the need for systems that …

In the last few years, a number of NLP researchers have developed and participated in the
task of Recognizing Textual Entailment (RTE). This task encapsulates Natural Language …

How do we scale information extraction to the massive size and unprecedented
heterogeneity of the Web corpus? Beginning in 2003, our KnowItAll project has sought to …

We propose a method for automatically labelling topics learned via LDA topic models. We
generate our label candidate set from the top-ranking topic terms, titles of Wikipedia articles …

Word meanings change over time and an automated procedure for extracting this
information from text would be useful for historical exploratory studies, information retrieval …

We explore unsupervised approaches to relation extraction between two named entities; for
instance, the semantic bornIn relation between a person and location entity. Concretely, we …