By design, word embeddings are unable to model the dynamic nature of words' semantics,
ie, the property of words to correspond to potentially different meanings. To address this …

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 …

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

In this paper I look at Natural Language Understanding, an area of Natural Language
Processing aimed at making sense of text, through the lens of a visionary future: what do we …

Integrating text and knowledge into a unified semantic space has attracted significant
research interests recently. However, the ambiguity in the common space remains a …

Word embedding, which represents individual words with semantically fixed-length vectors,
has made it possible to successfully apply deep learning to natural language processing …

Distributional semantics based on neural approaches is a cornerstone of Natural Language
Processing, with surprising connections to human meaning representation as well. Recent …

Contextualized word embeddings have boosted many NLP tasks compared with traditional
static word embeddings. However, the word with a specific sense may have different …

This paper presents a set of experiments to evaluate and compare between the performance
of using CBOW Word2Vec and Lemma2Vec models for Arabic Word-in-Context (WiC) …

Generating the precise semantic representation of a word or concept is a fundamental task
in natural language processing. Recent studies which incorporate semantic knowledge into …