  In Natural Language Processing, automatic extracting the meaning of texts
constitutes an important problem. Our focus is the computational analysis of
meaning of short scientific texts (abstracts or brief reports). In this paper,
a vector space model is developed for quantifying the meaning of words and
texts. We introduce the Meaning Space, in which the meaning of a word is
represented by a vector of Relative Information Gain (RIG) about the subject
categories that the text belongs to, which can be obtained from observing the
word in the text. This new approach is applied to construct the Meaning Space
based on Leicester Scientific Corpus (LSC) and Leicester Scientific
Dictionary-Core (LScDC). The LSC is a scientific corpus of 1,673,350 abstracts
and the LScDC is a scientific dictionary which words are extracted from the
LSC. Each text in the LSC belongs to at least one of 252 subject categories of
Web of Science (WoS). These categories are used in construction of vectors of
information gains. The Meaning Space is described and statistically analysed
for the LSC with the LScDC. The usefulness of the proposed representation model
is evaluated through top-ranked words in each category. The most informative n
words are ordered. We demonstrated that RIG-based word ranking is much more
useful than ranking based on raw word frequency in determining the
science-specific meaning and importance of a word. The proposed model based on
RIG is shown to have ability to stand out topic-specific words in categories.
The most informative words are presented for 252 categories. The new scientific
dictionary and the 103,998 x 252 Word-Category RIG Matrix are available online.
Analysis of the Meaning Space provides us with a tool to further explore
quantifying the meaning of a text using more complex and context-dependent
meaning models that use co-occurrence of words and their combinations.
