Domain portability and adaptation of NLP components and Word Sense Disambiguation systems present new challenges. The difficulties found by supervised systems to adapt might change the way we assess the strengths and weaknesses of supervised and knowledgebased WSD systems. Unfortunately, all existing evaluation datasets for specific domains are lexical-sample corpora. With this paper we want to motivate the creation of an allwords test dataset for WSD on the environment domain in several languages, and present the overall design of this SemEval task
Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a comp...
This article focuses on Word Sense Disambiguation (WSD), which is a Natural Lan-guage Processing tas...
Transformer-based architectures brought a breeze of change to Word Sense Disambiguation (WSD), impro...
Domain portability and adaptation of NLP components and Word Sense Disambiguation systems present ne...
Domain portability and adaptation of NLP components and Word Sense Disambiguation systems present ne...
Word Sense Disambiguation (WSD) is an important but challenging technique in the area of natural lan...
Natural language is inherently ambiguous. For example, the word "bank" can mean a financial institut...
Current Word Sense Disambiguation systems show an extremely poor performance on low frequent senses,...
The paper presents a flexible system for extracting features and creating training and testexamples ...
Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowled...
Current Word Sense Disambiguation systems show an extremely poor performance on low fre- quent sense...
Word Sense Disambiguation (WSD) is an important but challenging technique in the area of natural lan...
Word Sense Disambiguation (WSD) is an important but challenging technique in the area of natural lan...
With state-of-the-art systems having finally attained estimated human performance, Word Sense Disamb...
This article focuses on Word Sense Disambiguation (WSD), which is a Natural Language Processing task...
Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a comp...
This article focuses on Word Sense Disambiguation (WSD), which is a Natural Lan-guage Processing tas...
Transformer-based architectures brought a breeze of change to Word Sense Disambiguation (WSD), impro...
Domain portability and adaptation of NLP components and Word Sense Disambiguation systems present ne...
Domain portability and adaptation of NLP components and Word Sense Disambiguation systems present ne...
Word Sense Disambiguation (WSD) is an important but challenging technique in the area of natural lan...
Natural language is inherently ambiguous. For example, the word "bank" can mean a financial institut...
Current Word Sense Disambiguation systems show an extremely poor performance on low frequent senses,...
The paper presents a flexible system for extracting features and creating training and testexamples ...
Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowled...
Current Word Sense Disambiguation systems show an extremely poor performance on low fre- quent sense...
Word Sense Disambiguation (WSD) is an important but challenging technique in the area of natural lan...
Word Sense Disambiguation (WSD) is an important but challenging technique in the area of natural lan...
With state-of-the-art systems having finally attained estimated human performance, Word Sense Disamb...
This article focuses on Word Sense Disambiguation (WSD), which is a Natural Language Processing task...
Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a comp...
This article focuses on Word Sense Disambiguation (WSD), which is a Natural Lan-guage Processing tas...
Transformer-based architectures brought a breeze of change to Word Sense Disambiguation (WSD), impro...