Recent Trends in Word Sense Disambiguation : A Survey

Survey TrackPeer reviewe

Universal Semantic Annotator

Explicit semantic knowledge has often been considered a necessary ingredient to enable the development of intelligent systems. However, current stateof- the-art tools for the automatic extraction of such knowledge often require expert understanding of the complex techniques used in lexical and sentence-level semantics and their linguistic theories. To overcome this limitation and lower the barrier to entry, we present the Universal Semantic Annotator (USeA) ELG pilot project, which offers a transparent way to automatically provide high-quality semantic annotations in 100 languages through state-of-the-art models, making it easy to exploit semantic knowledge in real-world applications

WiC-ITA at EVALITA2023: Overview of the EVALITA2023 Word-in-Context for ITAlian Task

WiC-ita is a shared task proposed at the EVALITA 2023 campaign. The task focuses on the meaning of words in specific contexts and has been modelled as both a binary classification and a ranking problem. Overall, 4 groups took part in both subtasks, with 9 different runs. In this report, we describe how the task was set up, we report the system results, and we discuss them

A Semantic Unsupervised Learning Approach to Word Sense Disambiguation

Word Sense Disambiguation (WSD) is the identification of the particular meaning for a word based on the context of its usage. WSD is a complex task that is an important component of language processing and information analysis systems in several fields. The best current methods for WSD rely on human input and are limited to a finite set of words. Complicating matters further, language is dynamic and over time usage changes and new words are introduced. Static definitions created by previously defined analyses become outdated or are inadequate to deal with current usage. Fully automated methods are needed both for sense discovery and for distinguishing the sense being used for a word in context to efficiently realize the benefits of WSD across a broader spectrum of language. Latent Semantic Analysis (LSA) is a powerful automated unsupervised learning system that has not been widely applied in this area. The research described in this proposal will apply advanced LSA techniques in a novel way to the WSD tasks of sense discovery and distinguishing senses in use

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail

European Language Grid

This open access book provides an in-depth description of the EU project European Language Grid (ELG). Its motivation lies in the fact that Europe is a multilingual society with 24 official European Union Member State languages and dozens of additional languages including regional and minority languages. The only meaningful way to enable multilingualism and to benefit from this rich linguistic heritage is through Language Technologies (LT) including Natural Language Processing (NLP), Natural Language Understanding (NLU), Speech Technologies and language-centric Artificial Intelligence (AI) applications. The European Language Grid provides a single umbrella platform for the European LT community, including research and industry, effectively functioning as a virtual home, marketplace, showroom, and deployment centre for all services, tools, resources, products and organisations active in the field. Today the ELG cloud platform already offers access to more than 13,000 language processing tools and language resources. It enables all stakeholders to deposit, upload and deploy their technologies and datasets. The platform also supports the long-term objective of establishing digital language equality in Europe by 2030 – to create a situation in which all European languages enjoy equal technological support. This is the very first book dedicated to Language Technology and NLP platforms. Cloud technology has only recently matured enough to make the development of a platform like ELG feasible on a larger scale. The book comprehensively describes the results of the ELG project. Following an introduction, the content is divided into four main parts: (I) ELG Cloud Platform; (II) ELG Inventory of Technologies and Resources; (III) ELG Community and Initiative; and (IV) ELG Open Calls and Pilot Projects