Semantic categories of nominals for conceptual dependency analysis of natural language


Abstract: A system for the semantic categorization of conceptual objects (nominals) is provided. The system is intended to aid computer understanding of natural language. Specific implementations for noun-pairs and prepositional phrases are offered.

Semantic Message Detection for Machine Translation, Using an Interlingua


What is needed is a discipline which will study semantic message-connection in a way analogous to that in which metamathematics studies mathematical connection, and to that in which mathematical linguistics now studies syntactic connection. Research Used as Data for the Construction of T (a) Conceptual Dictionary for English The uses of the main words and phrases of English are mapped on to a classificatory system of about 750 descriptors, or heads, these heads being streamlined from Roget's Thesaurus. For Instance, a single card covers Disappoint, Disappointed, Disappointing, Disappointment. The two connectives, / ("slash") and: ("colon") and a word-order rule are used as in T to replace R.H. Richens' three subscripts, and every two pairs of elements are bracketted together, two bracketted pairs of elements counting as a single pair for the purpose of forming 2nd order brackets.

A design for an understanding machine


It also maintains that all human meaning may be exhaustively represented in terms of readings on a practically infinite number of calibrated standards, or, alternatively, by elaborate constellations of readings on a very small number of "element" standards. The resolution of a polysemantic ambiguity, by whatever method of translation, ultimately consists of exploiting clues in the words, sentences or paragraphs of text that surround the polysemantic word, clues which make certain of its alternate meanings impossible, and, generally, leave only one of its meanings appropriate for that particular context. Any English speaking human, upon encountering a sentence containing both "bank" and one or more of these clue words, will use the clue word's semantic content, if necessary, to help resolve the meaning of "bank". From there the machine would be programmed to utilize clues in the words surrounding "bank" which might be helpful for deciding which of that word's two meanings was appropriate in this case.