Linked open data: For NLP or by NLP?
|Linked open data: For NLP or by NLP?|
|Published in||PACLIC 25 - Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation|
|Keyword(s)||Unknown (Extra: Classification scheme, Collective intelligences, Computational thinkings, Content-based, Generation process, Hyperlinks, Information Extraction, Inter-relationships, Intrinsic property, Knowledge resource, Link structure, Linked datum, Social Networks, Structure-based, Synsets, Web Corpora, Wikipedia, Wordnet, Hypertext systems, Knowledge management, Websites, Natural language processing systems)|
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Linked open data: For NLP or by NLP? is a 2011 journal article written in English by Choi K.-S. and published in PACLIC 25 - Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation.
If we call Wikipedia or Wiktionary as "web knowledge resource", the question is about whether they can contribute to NLP itself and furthermore to the knowledge resource for knowledge-leveraged computational thinking. Comparing with the structure insideWordNet from the view of its human- encoded precise classification scheme, such web knowledge resource has category structure based on collectively generated tags and structures like infobox. They are called also as "Collectively Generated Content" and its structuralized content based on collective intelligence. It is heavily based on linking among terms and we also say that it is one member of linked data. The problem is in whether such collectively generated knowledge resource can contribute to NLP and how much it can be effective. The more clean primitives of linked terms in web knowledge resources will be assumed, based on the essential property of Guarino (2000) or intrinsic property of Mizoguchi (2004). The number of entries in web knowledge resources increases very fast but their inter-relationships are indirectly calculated by their link structure. We can imagine that their entries could be mapped to one of instances under some structure of primitive concepts, like synsets of WordNet. Let's name such primitives to be "intrinsic tokens" that are derived from collectively generated knowledge resource under the principles of intrinsic properties. The procedure could be approximately proven and it will be a kind of statistical logic. We then go to the issues about what area of NLP can be solved by the so-called intrinsic tokens and their relations, a resultant approximately generated primitives. Can NLP contribute to the user generation process of content? Consider the structure of infobox in Wikipedia more closely. It will be discussed about how NLP can help the population of relevant entries, like the social network mechanism for multi-lingual environment and information extraction purpose. The traditional NLP starts from words in text but now also works have been undergoing on the web corpus with hyperlinks and html markups. In web knowledge resources, the words and chunks have underlying URIs, a kind of annotation. It signals a new paradigm of NLP.
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