You and several other people on the Luhn paper. E.g., I linked to Rob Meredith’s Monash BI post — incorrectly ascribed to me by some people — last year, as per the above.

But I did miss your link above, making the same point as I did in this post, only months earlier. Well done!

Of course, now that I’ve seen it, I could quibble with some aspects. You seem to be more positive on the current state of irony detection than I am. And you seem to be asserting that sophisticated data mining algorithms go hand in hand with sophisticated semantic recognition, while I think the opposite is closer to the truth.

Best,

CAM

On this topic, the text analytics origins of BI, check out my October 2007 article, A Brief History of Text Analytics, http://www.b-eye-network.com/view/6311 . Quoting from it –

Text analytics is an answer to the “unstructured data” problem, which is best expressed by the truism that eighty percent of enterprise information originates and is locked in “unstructured” form. That problem has been recognized for decades. In fact, the first definition of business intelligence (BI) itself, in an October 1958 IBM Journal article by H.P. Luhn, A Business Intelligence System, describes a system that will:

“…utilize data-processing machines for auto-abstracting and auto-encoding of documents and for creating interest profiles for each of the ‘action points’ in an organization. Both incoming and internally generated documents are automatically abstracted, characterized by a word pattern, and sent automatically to appropriate action points.”

So we see that the earliest BI focus was on text – on extraction, categorization, and classification rather than on numerical data!

Yet as management information systems developed starting in the 1960s, and as BI emerged in the ’80s and ’90s as a software category and field of practice, the emphasis was on numerical data stored in relational databases. This is not surprising: text in “unstructured” documents is hard to process. We went after the low-hanging fruit – the fielded, numerical data – in response to the analytics imperative that any business process worth conducting should be measurable, and that any data worth collecting should be analyzed.

Seth