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  1. Proceedings of the 2013 international workshop on Mining unstructured big data using natural language processing (UnstructureNLP '13)
  2. Mining semantics for culturomics: towards a knowledge-based approach
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Big data opportunities and challenges for IR, text mining and NLP
Mining semantics for culturomics: towards a knowledge-based approach
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Mining semantics for culturomics: towards a knowledge-based approach

Content Provider ACM Digital Library
Author Kokkinakis, Dimitrios Johansson, Richard Forsberg, Markus Dubhashi, Devdatt Nugues, Pierre Borin, Lars
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.
Starting Page 3
Ending Page 10
Page Count 8
File Format PDF
ISBN 9781450324151
DOI 10.1145/2513549.2513551
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-10-28
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Language technology Semantic parsing
Content Type Text
Resource Type Article
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