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development of user interfaces, which by means of taxonomies lead the user
to the searched domain of interest (Dörre, Gerstl and Seiffert, 2004, p. 480).
One of the biggest challenges of information retrieval systems is the fact
that most (if not all) of the documents — in the broader sense of the word —
are written in a natural language ( 1 ). Among others these communication systems
are characterised by a certain number of different possibilities to refer to
the same extralinguistic facts. Sometimes it is even a good method in a communication
situation to reformulate a statement in order to show the understanding
of the original one and/or to get a conirmation of it. In other cases,
certain facts are paraphrased without a speciic keyword being used. for example,
if an author describes a nice lady in white clothes sitting on a glass
bowl, it could mean that he is talking about luck. this example of a medieval
allegory, which today is probably only understood by some experts, proves
another characteristic of human language: it changes over the course of time.
New expressions appear and others disappear or change their meaning.
It has also to be kept in mind that the items of our vocabulary are not only
in a certain relation with the concept of an extralinguistic fact, but are related
to each other as well. this is why, for instance, in good dictionaries you ind
hints about synonyms or antonyms to a given expression. But in the context
of environment protection, for example, it could also be of interest to retrieve
information on air pollution. this case indicates the complexity of the relations
between expressions, and it may be doubted if such word ields are ever
complete.
Such circumstances obviously make an automatic retrieval of performing
information extremely complex. So documents need to be analysed by means
of scientiic linguistic methodology which goes far beyond the still widespread
approach of simple text indexing and clearing. the still ‘young’ domain of text
mining tries to develop appropriate methods to support the digging for information
within natural language documents.
text mining is sometimes also referred to as ‘text data mining’ or ‘knowledge
discovery in text’. In general it deines the process of retrieving information
in texts. this is the most important difference between data mining and
text mining. while data mining procedures try to extract relevant information
from structured databases, text mining concentrates on unstructured text doc-
( 1 ) Some attempts to translate documents into a more formal language — Interlingua —
were not very successful. See the comments by hutchins (1986, Chapter 10).
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