Text Mining


Text Mining
Purpose
Humans are capable of using the Web to carry out tasks such as finding the
Finnish word for "cat", reserving a library book, and searching for a low price on a
DVD. However, a computer cannot accomplish the same tasks without human
direction because web pages are designed to be read by people, not machines. The
semantic web is a vision of information that is understandable by computers, so that
they can perform more of the tedious work involved in finding, sharing and
combining information on the web.
Text Mining:
Text mining, sometimes alternately referred to as text data mining, refers
generally to the process of deriving high quality information from text. High quality
information is typically derived through the dividing of patterns and trends through
means such as statistical pattern learning.

Text mining usually involves the process of structuring the input text
(usually parsing, along with the addition of some derived linguistic
features and the removal of others, and subsequent insertion into a
database), deriving patterns within the structured data, and finally
evaluation and interpretation of the output. 'High quality' in text
mining usually refers to some combination of relevance, novelty, and
interestingness. Typical text mining tasks include text categorization,
text clustering, concept/entity extraction, production of granular
taxonomies, sentiment analysis, document summarization, and entity
relation modeling (i.e., learning relations between named entities).
Land Allocation Decision Support System:
• LADSS or Land Allocation Decision Support System, is an agricultural land use
planning tool being developed at The Macaulay Institute. It is implemented using
the programming language from Gensym alongside a Smallworld GIS application
using the Magik programming language and an Oracle database.
• LADSS has recently been used to address climate change issues affecting
agriculture in Scotland and Italy. Part of this work has involved the use of General
Circulation Models (also known as Global climate models) to predict future climate
scenarios.

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