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Mining semantics for culturomics: towards a knowledge-based approach

Conference paper
Authors Lars Borin
Devdatt Dubhashi
Markus Forsberg
Richard Johansson
Dimitrios Kokkinakis
Pierre Nugues
Published in 2013 ACM International Workshop on Mining Unstructured Big Data Using Natural Language Processing, UnstructureNLP 2013, Held at 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013; San Francisco, CA; United States; 28 October 2013 through 28 October 2013
Pages 3-10
ISBN 978-1-4503-2415-1
Publication year 2013
Published at Department of Swedish
Pages 3-10
Language en
Links dx.doi.org/10.1145/2513549.2513551
Keywords språkteknologi
Subject categories Language Technology (Computational Linguistics)

Abstract

The massive amounts of text data made available through the Google Books digitization project have inspired a new field of big-data textual research. Named culturomics, this field has attracted the attention of a growing number of scholars over recent years. However, initial studies based on these data have been criticized for not referring to relevant work in linguistics and language technology. This paper provides some ideas, thoughts and first steps towards a new culturomics initiative, based this time on Swedish data, which pursues a more knowledge-based approach than previous work in this emerging field. The amount of new Swedish text produced daily and older texts being digitized in cultural heritage projects grows at an accelerating rate. These volumes of text being available in digital form have grown far beyond the capacity of human readers, leaving automated semantic processing of the texts as the only realistic option for accessing and using the information contained in them. The aim of our recently initiated research program is to advance the state of the art in language technology resources and methods for semantic processing of Big Swedish text and focus on the theoretical and methodological advancement of the state of the art in extracting and correlating information from large volumes of Swedish text using a combination of knowledge-based and statistical methods.

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