Language understanding research is held back by a failure to relate language to the
physical world it describes and to the social interactions it facilitates. Despite the incredible …

Knowledge representation learning (KRL) aims to represent entities and relations in
knowledge graph in low-dimensional semantic space, which have been widely used in …

Learning graph representations via low-dimensional embeddings that preserve relevant
network properties is an important class of problems in machine learning. We here present a …

Detecting hypernymy relations is a key task in NLP, which is addressed in the literature
using two complementary approaches. Distributional methods, whose supervised variants …

Distributional representations of words have been recently used in supervised settings for
recognizing lexical inference relations between word pairs, such as hypernymy and …

Technical debt is a metaphor to reflect the tradeoff software engineers make between short-
term benefits and long-term stability. Self-admitted technical debt (SATD), a variant of …

Recent work on word embeddings has shown that simple vector subtraction over pre-trained
embeddings is surprisingly effective at capturing different lexical relations, despite lacking …

State-of-the-art targeted language understanding systems rely on deep learning methods
using 1-hot word vectors or off-the-shelf word embeddings. While word embeddings can be …

Using the frequency of keywords is a classic approach in the formal analysis of text, but has
the drawback of glossing over the relationality of word meanings. Word embedding models …

This paper describes the SemEval 2018 Shared Task on Hypernym Discovery. We put
forward this task as a complementary benchmark for modeling hypernymy, a problem which …