History
Labor-intensive manual text mining approaches first surfaced in the mid-1980s,Template:Http://www.ppc.sas.upenn.edu/cave.htm but technological advances have enabled the field to advance during the past decade. Text mining is an interdisciplinary field that draws on information retrieval, data mining, machine learning, statistics, and computational linguistics. As most information (common estimates say over 80%) is currently stored as text, text mining is believed to have a high commercial potential value. Increasing interest is being paid to multilingual data mining: the ability to gain information across languages and cluster similar items from different linguistic sources according to their meaning.
The challenge of exploiting the large proportion of enterprise information that originates in "unstructured" form has been recognized for decades. It is recognized in the earliest definition of business intelligence (BI), in an October 1958 IBM Journal article by H.P. Luhn, A Business Intelligence System, which 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."
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. The emergence of text analytics in its current form stems from a refocusing of research in the late 1990s from algorithm development to application, as described by Prof. Marti A. Hearst in the paper Untangling Text Data Mining:
For almost a decade the computational linguistics community has viewed large text collections as a resource to be tapped in order to produce better text analysis algorithms. In this paper, I have attempted to suggest a new emphasis: the use of large online text collections to discover new facts and trends about the world itself. I suggest that to make progress we do not need fully artificial intelligent text analysis; rather, a mixture of computationally-driven and user-guided analysis may open the door to exciting new results.
Hearst's 1999 statement of need fairly well describes the state of text analytics technology and practice a decade later.
Read more about this topic: Text Mining
Famous quotes containing the word history:
“The history of reform is always identical; it is the comparison of the idea with the fact. Our modes of living are not agreeable to our imagination. We suspect they are unworthy. We arraign our daily employments.”
—Ralph Waldo Emerson (1803–1882)
“If you look at history you’ll find that no state has been so plagued by its rulers as when power has fallen into the hands of some dabbler in philosophy or literary addict.”
—Desiderius Erasmus (c. 1466–1536)
“To care for the quarrels of the past, to identify oneself passionately with a cause that became, politically speaking, a losing cause with the birth of the modern world, is to experience a kind of straining against reality, a rebellious nonconformity that, again, is rare in America, where children are instructed in the virtues of the system they live under, as though history had achieved a happy ending in American civics.”
—Mary McCarthy (1912–1989)