1 resultado para named inventories
em Bulgarian Digital Mathematics Library at IMI-BAS
Filtro por publicador
- JISC Information Environment Repository (1)
- Repository Napier (3)
- ABACUS. Repositorio de Producción Científica - Universidad Europea (1)
- Aberystwyth University Repository - Reino Unido (1)
- Adam Mickiewicz University Repository (2)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (2)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (2)
- Applied Math and Science Education Repository - Washington - USA (2)
- Aquatic Commons (32)
- Archive of European Integration (7)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (16)
- Aston University Research Archive (4)
- B-Digital - Universidade Fernando Pessoa - Portugal (2)
- Biblioteca Digital da Câmara dos Deputados (3)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (2)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (1)
- Biblioteca Digital de la Universidad Católica Argentina (8)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (51)
- Biodiversity Heritage Library, United States (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (8)
- Boston University Digital Common (3)
- Brock University, Canada (1)
- Bulgarian Digital Mathematics Library at IMI-BAS (1)
- CaltechTHESIS (12)
- Cambridge University Engineering Department Publications Database (9)
- CentAUR: Central Archive University of Reading - UK (1)
- Center for Jewish History Digital Collections (13)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (199)
- Cochin University of Science & Technology (CUSAT), India (2)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (4)
- CORA - Cork Open Research Archive - University College Cork - Ireland (5)
- DI-fusion - The institutional repository of Université Libre de Bruxelles (7)
- DigitalCommons@The Texas Medical Center (2)
- DRUM (Digital Repository at the University of Maryland) (3)
- Duke University (5)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (22)
- Fachlicher Dokumentenserver Paedagogik/Erziehungswissenschaften (1)
- Greenwich Academic Literature Archive - UK (5)
- Harvard University (2)
- Helda - Digital Repository of University of Helsinki (50)
- Indian Institute of Science - Bangalore - Índia (75)
- Instituto Politécnico de Castelo Branco - Portugal (1)
- Instituto Politécnico do Porto, Portugal (1)
- Massachusetts Institute of Technology (3)
- Memoria Académica - FaHCE, UNLP - Argentina (3)
- Memorial University Research Repository (1)
- Open University Netherlands (1)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (7)
- Portal de Revistas Científicas Complutenses - Espanha (1)
- Publishing Network for Geoscientific & Environmental Data (3)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (99)
- Queensland University of Technology - ePrints Archive (226)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (7)
- School of Medicine, Washington University, United States (2)
- Universidad de Alicante (1)
- Universidad Politécnica de Madrid (5)
- Universidade Complutense de Madrid (1)
- Universidade de Lisboa - Repositório Aberto (1)
- University of Michigan (36)
- University of Queensland eSpace - Australia (1)
- University of Washington (1)
- Worcester Research and Publications - Worcester Research and Publications - UK (1)
Resumo:
One of the ultimate aims of Natural Language Processing is to automate the analysis of the meaning of text. A fundamental step in that direction consists in enabling effective ways to automatically link textual references to their referents, that is, real world objects. The work presented in this paper addresses the problem of attributing a sense to proper names in a given text, i.e., automatically associating words representing Named Entities with their referents. The method for Named Entity Disambiguation proposed here is based on the concept of semantic relatedness, which in this work is obtained via a graph-based model over Wikipedia. We show that, without building the traditional bag of words representation of the text, but instead only considering named entities within the text, the proposed method achieves results competitive with the state-of-the-art on two different datasets.