Text data mining

Text data mining concerns the application of data mining to unstructured textual data. Our work stresses on using information extraction to first extract a structured database from a quantity of natural language texts and then discovering patterns in the resulting database using traditional KDD tools. It also concerns record linkage, a form of data-cleaning that recognizes equivalent but textually distinct items in the extracted data prior to mining. It is also related to our research on natural language learning.

The potentials for data mining from large text collections are effectively untapped. Text expresses a massive, rich range of information, but encrypts this information in a form that is difficult to decode automatically. Perhaps for this reason, there has been little work in text data mining to date, and most people who have talked about it have either conflated it with information access or have not made use of text directly to discover here to for unknown information.

The embryonic field of text data mining (TDM) has the peculiar difference of having a name and a fair amount of publicity but as yet almost no practitioners. I suspect this has happened because people assume TDM is a natural extension of the marginally less embryonic field of data mining (DM), also known as knowledge discovery in databases [Fayyad and Uthurusamy1999], and information archeology [Brachman et al.1993]. Additionally, there are some differences about what really establishes data mining. It turns out that ``mining'' is not a very good symbol for what people in the field actually do. Mining suggests removing precious pieces of ore from otherwise worthless rock. If data mining really followed this metaphor, it would mean that people were discovering new factoids within their inventory databases. Still, in practice this is not really the case. Instead, data mining applications tend to be (semi)automated discovery of trends and patterns across very large datasets, usually for the purposes of decision making .Part of what I wish to argue here is that in the case of text, it can be interesting to take the mining-for-nuggets metaphor extremely.

Data mining is a growing trend. All of the hardware and software required has been developing rapidly over the last few decades. Data mining will be the way of the future. Almost any kind of information can be found through using research procedures to extract what the user is looking for. Utilizing the procedures involves 'engineering' a search to find what you want.

The new practice known as "data mining" gets a lot of mileage in university settings. Numerous majors involve data mining as a business skill, and for learning about the topics featured in undergraduate and graduate programs. Sociology majors will be chiefly likely to do data mining as part of their program. Academic data mining assistances students in more than one way and can be an important part of the work that is done for a college degree.

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