Word embedding models have become a fundamental component in a wide range of
Natural Language Processing (NLP) applications. However, embeddings trained on human …

Over the past years, distributed semantic representations have proved to be effective and
flexible keepers of prior knowledge to be integrated into downstream applications. This …

Heterogeneous information networks (HINs) are ubiquitous in real-world applications. Due
to the heterogeneity in HINs, the typed edges may not fully align with each other. In order to …

Vector-based word representation paradigms situate lexical meaning at different levels of
abstraction. Distributional and static embedding models generate a single vector per word …

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 …

Word embeddings are widely used in Natural Language Processing, mainly due to their
success in capturing semantic information from massive corpora. However, their creation …

We present a novel semi-supervised approach for sequence transduction and apply it to
semantic parsing. The unsupervised component is based on a generative model in which …

Homographs, words with different meanings but the same surface form, have long caused
difficulty for machine translation systems, as it is difficult to select the correct translation …

Solving text classification in a weakly supervised manner is important for real-world
applications where human annotations are scarce. In this paper, we propose to query a …

Lexical ambiguity can impede NLP systems from accurate understanding of semantics.
Despite its potential benefits, the integration of sense-level information into NLP systems has …