Results 191 to 200 of about 1,924,192 (245)

Domain-wide Mapping of Peer-reviewed Literature for Genetic Developmental Disorders using Machine Learning and Gene2Phenotype

open access: yes
Yates TM   +10 more
europepmc   +1 more source

Word sense disambiguation for low resource languages: setswana collocations

open access: closedInternational Workshop on Signal Processing and Machine Learning (WSPML 2023), 2023
Word sense disambiguation (WSD) is a critical task in natural language processing (NLP) and artificial intelligence. Supervised methods, such as decision list algorithms, are considered the most accurate machine learning algorithms for WSD. However, they
Boago Okgetheng   +2 more
semanticscholar   +3 more sources

Effective Entity Disambiguation in Low-Resource Languages: A Study of Icelandic

open access: closed2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2023
Entity disambiguation (ED) is integral to the task of entity linking (EL), the task of identifying named entities in text and linking them to their corresponding entries in a knowledge base (KB).
Valdimar Ágúst Eggertsson   +3 more
semanticscholar   +3 more sources

PosWSD: Low-Resource Word Sense Disambiguation Model using Part Of Speech Information

open access: closed2022 International Conference on Asian Language Processing (IALP), 2022
Word Sense Disambiguation(WSD) is a long-standing problem in Natural Language Processing(NLP), which aims to find the exact meaning of the target word in a given context. Current WSD methods mainly rely on pre-trained models.
Yazhen Chen, Jian Zhang, Qipeng He
semanticscholar   +3 more sources

Keyword-Driven Resource Disambiguation over RDF Knowledge Bases

open access: closedJoint International Conference of Semantic Technology, 2013
Keyword search is the most popular way to access information. In this paper we introduce a novel approach for determining the correct resources for user-supplied queries based on a hidden Markov model. In our approach the user-supplied query is modeled as the observed data and the background knowledge is used for parameter estimation.
Saeedeh Shekarpour   +2 more
semanticscholar   +4 more sources

Improved Unsupervised Statistical Machine TranslationviaUnsupervised Word Sense Disambiguation for a Low-Resource and Indic Languages

open access: closedJournal of the Institution of Electronics and Telecommunication Engineers, 2022
Besides word order, word choice is a key stumbling block for machine translation (MT) in morphologically rich languages due to homonyms and polysemous difficulties.
Shefali Saxena   +3 more
openalex   +2 more sources

Named Entity Disambiguation for Resource-Poor Languages

open access: closedProceedings of the Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval, 2015
Named entity disambiguation (NED) is the task of linking ambiguous names in natural language text to canonical entities like people, organizations or places, registered in a knowledge base. The problem is well-studied for English text, but few systems have considered resource-poor languages that lack comprehensive name-entity dictionaries, entity ...
Mohamed H. Gad-Elrab   +2 more
semanticscholar   +4 more sources

A Novel Approach to Word Sense Disambiguation for a Low-Resource Morphologically Rich Language

open access: closedConference Information and Communication Technology, 2022
Ambiguity amongst words is common in every language. Thus in the world of Natural Language Processing(NLP), a very critical problem is one of Word Sense Disambiguation(WSD).
Puberun Boruah
openalex   +2 more sources

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