Text simplification (TS) reduces the complexity of the text to improve its readability and
understandability, while possibly retaining its original information content. Over time, TS has …

Word sense disambiguation (WSD) is the ability to identify the meaning of words in context
in a computational manner. WSD is considered an AI-complete problem, that is, a task …

Deep learning has unlocked new paths towards the emulation of the peculiarly-human
capability of learning from examples. While this kind of bottom-up learning works well for …

With the recent development of deep learning, research in AI has gained new vigor and
prominence. While machine learning has succeeded in revitalizing many research fields …

Context representations are central to various NLP tasks, such as word sense
disambiguation, named entity recognition, coreference resolution, and many more. In this …

Vector‐based models of word meaning have become increasingly popular in cognitive
science. The appeal of these models lies in their ability to represent meaning simply by …

In the last few years, a number of NLP researchers have developed and participated in the
task of Recognizing Textual Entailment (RTE). This task encapsulates Natural Language …

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

Although powerful pretrained language models generate high-quality output text, they bring
new concerns about the potential misuse of such models for malicious purposes. Natural …

Previous studies on lexical substitution tend to obtain substitute candidates by finding the
target word's synonyms from lexical resources (eg, WordNet) and then rank the candidates …