Sentiment analysis is a task that highly depends on the understanding of word senses.
Traditional neural network models are black boxes that represent word senses as vectors …

Pretrained Language Models (PLMs) learn rich cross-lingual knowledge and can be
finetuned to perform well on diverse tasks such as translation and multilingual word sense …

Abstract Several Natural Language Understanding (NLU) tasks focus on linking text to
explicit knowledge, including Word Sense Disambiguation, Semantic Role Labeling …

Lexical ambiguity is a significant and pervasive challenge in Neural Machine Translation
(NMT), with many state-of-the-art (SOTA) NMT systems struggling to handle polysemous …

Translating text that contains entity names is a challenging task, as cultural-related
references can vary significantly across languages. These variations may also be caused by …

As one of the oldest applications of natural language processing, machine translation (MT)
has a growing impact on human lives both as an end application and as a key component of …

In the era of deep learning, the natural language processing (NLP) community has become
increasingly reliant on large language models (LLM), which are essentially probabilistic …

NLU tasks link text to explicit knowledge, such as Word Sense Disambiguation, Semantic
Role Labeling, Semantic Parsing, and Relation Extraction. Integrating curated knowledge …

概要単語が複数の意味 (語義) を持つことを単語の多義性という. 機械が人間の言語を理解する上
で単語の多義性を適切に取り扱うことは重要な問題である. 単語の多義性に関する代表的な研究に …