Publication

Automatic information extraction from historical collections: the case of the 1808 venetian cadaster

2018
Talks

Abstract

The presentation reports on the on-going work to automatically process heterogeneous historical documents. After a quick overview of the general processing pipeline, a few examples are more comprehensively described. The recent progress in making large collections of digitised documents searchable is also be presented through the results of the automatic transcription of named entities in 18th century venetian fiscal documents. Finally, the case of the 1808 venetian cadaster is used to illustrate the general approach and the results of the processing of the whole 1808 cadaster are presented.

Official source

https://infoscience.epfl.ch/entities/publication/f4e26d36-8287-4af0-90db-c1df2d3d997e

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2018
Talks

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/f4e26d36-8287-4af0-90db-c1df2d3d997e

About this result

Related concepts (14)

Entity linking

In natural language processing, entity linking, also referred to as named-entity linking (NEL), named-entity disambiguation (NED), named-entity recognition and disambiguation (NERD) or named-entity normalization (NEN) is the task of assigning a unique identity to entities (such as famous individuals, locations, or companies) mentioned in text. For example, given the sentence "Paris is the capital of France", the idea is to determine that "Paris" refers to the city of Paris and not to Paris Hilton or any other entity that could be referred to as "Paris".

Information extraction

Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction Due to the difficulty of the problem, current approaches to IE (as of 2010) focus on narrowly restricted domains.

Knowledge extraction

Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, s) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL (data warehouse), the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema.

Related publications (4)

Automatic information extraction from historical collections: the case of the 1808 venetian cadaster

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