Semantic representation in many cases turns out to be universal, i.e., common to different natural languages. Purely grammatical features of different languages are not usually reflected in this representation. For example, the gender of Spanish nouns and adjectives is not included in their semantic representation, so that this representation turned to be equal to that of English. If the given noun refers to a person of a specific sex, the latter is reflected on semantic level explicitly, via a special predicate of sex, and it is on the grammar of specific language where is established the correspondence between sex and gender. It is curious that in German nouns can have three genders: masculine, feminine, and neuter, but the noun Mädchen girl is neuter, not feminine!
Thus, the semantic representation of the English sentence The little girls see the red flower it is the same as the one given above, despite the absence of gender in English nouns and adjectives. The representation of the corresponding Russian sentence is the same too, though the word used for red in Russian has masculine gender, because of its agreement in gender with corresponding noun of masculine.[16]
Nevertheless, the cases when semantic representations for two or more utterances with seemingly the same meaning do occur. In such situations, linguists hope to find a universal representation via decomposition and even atomization of the meaning of several semantic components.
In natural sciences, such as physics, researchers usually try to divide all the entities under consideration into the simplest possible, i.e., atomic, or elementary, units and then to deduce properties of their conglomerations from the properties of these elementary entities. In principle, linguistics has the same objective. It tries to find the atomic elements of meaning usually called semantic primitives, or semes.
Semes are considered indefinable, since they cannot be interpreted in terms of any other linguistic meanings. Nevertheless, they can be explained to human readers by examples from the extralinguistic reality, such as pictures, sound records, videos, etc. All other components of semantic representation should be then expressed through the semes.
In other words, each predicate or its terms can be usually represented in the semantic representation of text in a more detailed manner, such as a logical formula or a semantic graph. For example, we can decompose
MATAR (x) CAUSAR (MORIR (x)) CAUSAR (CESAR (VIVIR (x))),
i.e., MATAR (x) is something like causar cesar el vivir (x), or cause stop living (x), where the predicates CESAR (x), VIVIR (y), and CAUSAR (z) are more elementary than the initial predicate MATAR (x).[17]
Figure IV.9 shows a decomposition of the sentence Juan mató a José enseguida = Juan causó a José cesar vivir enseguida in the mentioned more primitive notions. Note that the number labels of valencies of the whole combination of the primitives can differ from the number labels of corresponding valencies of the member primitives: e.g., the actant 2 of the whole combination is the actant 1 of the component VIVIR. The mark C in Figure IV.9 stands for the circumstantial relation (which is not a valency but something inverse, i.e., a passive semantic valency).
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FIGURE IV.9. Decomposition of the verb MATAR into semes.
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Over the past 30 years, ambitious attempts to find and describe a limited number of semes, to which a major part of the semantics of a natural language would be reduced, have not been successful.
Some scientists agree that the expected number of such semes is not much more than 2´000, but until now, this figure is still debatable. To comply with needs of computational linguistics, everybody agreed that it is sufficient to disintegrate meanings of lexemes to a reasonable limit implied by the application.
Therefore, computational linguistics uses many evidently non-elementary terms and logical predicates in the semantic representation. From this point of view, the translation from one cognate language to another does not need any disintegration of meaning at all.
Once again, only practical results help computational linguists to judge what meaning representation is the best for the selected application domain.
