A taxonomy is a semantic hierarchy, consisting of concepts linked by is-a relations. While a
large number of taxonomies have been constructed from human-compiled resources (eg …

Understanding human language has been a sub-challenge on the way of intelligent
machines. The study of meaning in natural language processing (NLP) relies on the …

We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that
transforms any input word vector space to emphasise the asymmetric relation of lexical …

We describe a new deep learning architecture for learning to rank question answer pairs.
Our approach extends the long short-term memory (LSTM) network with holographic …

We present a novel neural model HyperVec to learn hierarchical embeddings for hypernymy
detection and directionality. While previous embeddings have shown limitations on …

Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for
many web applications. For example, online retailers (eg, Amazon and eBay) use …

Automatic construction of a taxonomy supports many applications in e-commerce, web
search, and question answering. Existing taxonomy expansion or completion methods …

The automated construction of topic taxonomies can benefit numerous applications,
including web search, recommendation, and knowledge discovery. One of the major …

Taxonomy construction is not only a fundamental task for semantic analysis of text corpora,
but also an important step for applications such as information filtering, recommendation …

Taxonomies are fundamental to many real-world applications in various domains, serving as
structural representations of knowledge. To deal with the increasing volume of new concepts …