There is also evidence of renewed interest in the methods and tools being developed by linguists, particularly corpus linguists, in areas such as text mining, corpus annotation, automated tagging and historical thesauri.
This is one of my favourite topics. I find it incredibly interesting and relevant. It is very difficult and algorithm based.
the database, particularly in relational form
this data is the biggest advantage, computers are good at tedious tasks that humans just arent. going through big data is one of those tasks
History and computing
found through footnotes #2
shows the amount of free data available and how overwhelming it can seem
80 terabytes of archived web crawl data available for research
footnotes #1
For computer scientists, they are often focused not just on materials of a scope that can’t be read, but on volumes of information that elude processing by conventional computer systems, such as Google’s collection or the shocking amount of information generated by experiments such as CERN’s Large Hadron Collider.
This is one of the reason NPL is so interesting. Able to go through insane amounts of data with a combination of computer science, linguistics and the domain (in this case history)
macroscope
reminds me a little bit of okhams razor, "the more assumptions you have to make, the more unlikely an explanation is" or "the simple explanation is always the best". The more complex the explanation is the less clear it is.
enter the shortcode that will grab all of your public annotations: [hypothesis user = 'kris.shaffer'] w
this is so cool, so helpful for research and web design
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