Distributional semantics provides multidimensional, graded, empirically induced word
representations that successfully capture many aspects of meaning in natural languages, as …

We describe a question answering model that applies to both images and structured
knowledge bases. The model uses natural language strings to automatically assemble …

Matrix factorization approaches to relation extraction provide several attractive features: they
support distant supervision, handle open schemas, and leverage unlabeled data …

Rule-based models are attractive for various tasks because they inherently lead to
interpretable and explainable decisions and can easily incorporate prior knowledge …

We introduce HyperLex—a data set and evaluation resource that quantifies the extent of the
semantic category membership, that is, type-of relation, also known as hyponymy …

This chapter critically examines the potential contributions of modern language models to
theoretical linguistics. Despite their focus on engineering goals, these models' ability to …

Abstract Recognizing Textual Entailment (RTE) is a fundamentally important task in natural
language processing that has many applications. The recently released Stanford Natural …

As language and visual understanding by machines progresses rapidly, we are observing
an increasing interest in holistic architectures that tightly interlink both modalities in a joint …

Commonsense reasoning patterns such as interpolation and a fortiori inference have proven
useful for dealing with gaps in structured knowledge bases. An important difficulty in …

In this paper, we introduce an approach to automatically map a standard distributional
semantic space onto a set-theoretic model. We predict that there is a functional relationship …