Text data has been growing drastically in the present day because of digitalization. The
Internet, being flooded with millions of documents every day, makes the task of text …

Over the last two decades, determining the similarity between words as well as between
their meanings, that is, word senses, has been proven to be of vital importance in the field of …

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 …

Most word embedding models typically represent each word using a single vector, which
makes these models indiscriminative for ubiquitous homonymy and polysemy. In order to …

Most word representation methods assume that each word owns a single semantic vector.
This is usually problematic because lexical ambiguity is ubiquitous, which is also the …

We present\textit {AutoExtend}, a system to learn embeddings for synsets and lexemes. It is
flexible in that it can take any word embeddings as input and does not need an additional …

We introduce Probabilistic FastText, a new model for word embeddings that can capture
multiple word senses, sub-word structure, and uncertainty information. In particular, we …

While GPGPU stands for general-purpose computation on graphics processing units, the
lack of explicit support for inter-block communication on the GPU arguably hampers its …

The recently proposed Skip-gram model is a powerful method for learning high-dimensional
word representations that capture rich semantic relationships between words. However …

Sememes are minimum semantic units of word meanings, and the meaning of each word
sense is typically composed by several sememes. Since sememes are not explicit for each …