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Found 11 papers, 2 papers with code
Named Entity Recognition and Classification on Historical Documents: A Survey
After decades of massive digitisation, an unprecedented amount of historical documents is available in digital format, along with their machine-readable texts.
Language Resources for Historical Newspapers: the Impresso Collection
If this represents a huge step forward in terms of preservation and accessibility, the next fundamental challenge{--} and real promise of digitization{--} is to exploit the contents of these digital assets, and therefore to adapt and develop appropriate language technologies to search and retrieve information from this {`}Big Data of the Past{'}.
Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers
The massive amounts of digitized historical documents acquired over the last decades naturally lend themselves to automatic processing and exploration.
Cross-lingual Linking of Multi-word Entities and their corresponding Acronyms
This paper reports on an approach and experiments to automatically build a cross-lingual multi-word entity resource.
Named Entity Resources - Overview and Outlook
Recognition of real-world entities is crucial for most NLP applications.
Media monitoring and information extraction for the highly inflected agglutinative language Hungarian
In this paper, we describe the effort of adding to EMM Hungarian text mining tools for news gathering; document categorisation; named entity recognition and classification for persons, organisations and locations; name lemmatisation; quotation recognition; and cross-lingual linking of related news clusters.
Clustering of Multi-Word Named Entity variants: Multilingual Evaluation
Multi-word entities, such as organisation names, are frequently written in many different ways.
Representing Multilingual Data as Linked Data: the Case of BabelNet 2.0
Recent years have witnessed a surge in the amount of semantic information published on the Web.
Acronym recognition and processing in 22 languages
We are presenting work on recognising acronyms of the form Long-Form (Short-Form) such as "International Monetary Fund (IMF)" in millions of news articles in twenty-two languages, as part of our more general effort to recognise entities and their variants in news text and to use them for the automatic analysis of the news, including the linking of related news across languages.
