Lexical Semantics of Adjectives - CiteSeerX

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ture this in their entries by grouping all senses into a small number of “main” ones which are further
divided, often iteratively. Thus, Webster (80) has only two main senses and two levels of specification
under them, but American Heritage (81) prefers putting 20 senses on the top level, with minimum
further subdivision. Both from the point of view of theoretical linguistics (the essential
complementary distribution principle) and of natural language processing, entries like (81) are the
least helpful.
The objections in (82) push us in an obvious direction: we see good as having one sense, which
takes different shades, depending on the meaning of the modified nouns. This sense of good is
something like “assigning a high positive value range” to a selected property of the noun. Our entry
for good (45) captures this meaning but refuses to specify the noun property, and we have a good
reason for not doing that (see Section 4.2 below). Good is, of course, an adjective with a very
broadly applicable meaning, but the same objections to excessive polysemy hold for other adjectives
as well. The same principle of polysemy reduction pertains to other lexical categories: thus,
in Nirenburg et al (1995), we reduced 52 listed senses for the Spanish verb dejar to a manageable
set of just 7.
4.2 Grain Size
Reducing the number of senses in a polysemous lexical item affects the grain size of its semantic
representation: the fewer the number the larger the grain size. We are interested in formulating
principles of granularity which will allow us to keep the number of senses in an entry low.
4.2.1 Grain Size on a Principled Basis: Practical Effability
The grain size of lexical representation in an NLP system must be as follows:
(84) (i) principle-based
(ii) application-oriented
The principles which guided the acquisition of the MikroKosmos lexicon entries for adjectives are
those formulated in (83) above. It is certain that more principles were used in this process. However,
they still await their formulation due to the usual difficulties with explicating intuitions. In
fact, we have imposed yet another principle which we expect to remove in later work. For reasons
of simplicity of acquisition, we have strived to maintain the grain size of description at the same
level of detailization throughout the system. In fact, however, variable-depth meaning descriptions
(see Nirenburg and Raskin 1986) are necessary when one wants to balance economy of effort and
ability to disambiguate.
It has been demonstrated in various schools of semantics that natural languages dissect reality differently
and fill their words with different chunks of that reality (see, for instance, Hjelmslev 1959,
Whorf 1956, Hayakawa 1975). It has also been experienced by translators, who know that wordfor-word
rendition of the text from one language to another never makes sense. This is the reason
for crucial semantic difficulties in MT, necessitating the kind of meaning analysis we are engaged
in here.
A well-evolved interlingua resulting from such meaning analysis creates lexicons for the source
and target languages which represent enough different senses of the words and other lexical items
to give serious credence, demonstrated in practice, to a hope that a meaning expressed in one language
will be expressed in another language in the same grain size without much difficulty--in