  One major problem in Natural Language Processing is the automatic analysis
and representation of human language. Human language is ambiguous and deeper
understanding of semantics and creating human-to-machine interaction have
required an effort in creating the schemes for act of communication and
building common-sense knowledge bases for the 'meaning' in texts. This paper
introduces computational methods for semantic analysis and the quantifying the
meaning of short scientific texts. Computational methods extracting semantic
feature are used to analyse the relations between texts of messages and
'representations of situations' for a newly created large collection of
scientific texts, Leicester Scientific Corpus. The representation of
scientific-specific meaning is standardised by replacing the situation
representations, rather than psychological properties, with the vectors of some
attributes: a list of scientific subject categories that the text belongs to.
First, this paper introduces 'Meaning Space' in which the informational
representation of the meaning is extracted from the occurrence of the word in
texts across the scientific categories, i.e., the meaning of a word is
represented by a vector of Relative Information Gain about the subject
categories. Then, the meaning space is statistically analysed for Leicester
Scientific Dictionary-Core and we investigate 'Principal Components of the
Meaning' to describe the adequate dimensions of the meaning. The research in
this paper conducts the base for the geometric representation of the meaning of
texts.
