May 8, 1995
We define {\em semantic complexity} using a new concept of {\em meaning automata}. We measure the semantic complexity of understanding of prepositional phrases, of an "in depth understanding system", and of a natural language interface to an on-line calendar. We argue that it is possible to measure some semantic complexities of natural language processing systems before building them, and that systems that exhibit relatively complex behavior can be built from semantically simple components.
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July 13, 1996
We introduce a method for analyzing the complexity of natural language processing tasks, and for predicting the difficulty new NLP tasks. Our complexity measures are derived from the Kolmogorov complexity of a class of automata --- {\it meaning automata}, whose purpose is to extract relevant pieces of information from sentences. Natural language semantics is defined only relative to the set of questions an automaton can answer. The paper shows examples of complexity estim...
May 29, 2015
In this introductory article we present the basics of an approach to implementing computational interpreting of natural language aiming to model the meanings of words and phrases. Unlike other approaches, we attempt to define the meanings of text fragments in a composable and computer interpretable way. We discuss models and ideas for detecting different types of semantic incomprehension and choosing the interpretation that makes most sense in a given context. Knowledge repre...
May 13, 2016
This paper is a reflexion on the computability of natural language semantics. It does not contain a new model or new results in the formal semantics of natural language: it is rather a computational analysis of the logical models and algorithms currently used in natural language semantics, defined as the mapping of a statement to logical formulas - formulas, because a statement can be ambiguous. We argue that as long as possible world semantics is left out, one can compute th...
September 10, 2021
In this paper we examine different meaning representations that are commonly used in different natural language applications today and discuss their limits, both in terms of the aspects of the natural language meaning they are modelling and in terms of the aspects of the application for which they are used.
January 17, 1995
We present an approach to natural language understanding based on a computable grammar of constructions. A "construction" consists of a set of features of form and a description of meaning in a context. A grammar is a set of constructions. This kind of grammar is the key element of Mincal, an implemented natural language, speech-enabled interface to an on-line calendar system. The system consists of a NL grammar, a parser, an on-line calendar, a domain knowledge base (about d...
August 24, 2019
Measuring text complexity is an essential task in several fields and applications (such as NLP, semantic web, smart education, etc.). The semantic layer of text is more tacit than its syntactic structure and, as a result, calculation of semantic complexity is more difficult than syntactic complexity. While there are famous and powerful academic and commercial syntactic complexity measures, the problem of measuring semantic complexity is still a challenging one. In this paper,...
August 18, 2011
For a system to understand natural language, it needs to be able to take natural language text and answer questions given in natural language with respect to that text; it also needs to be able to follow instructions given in natural language. To achieve this, a system must be able to process natural language and be able to capture the knowledge within that text. Thus it needs to be able to translate natural language text into a formal language. We discuss our approach to do ...
February 28, 2014
Over the past 50 years many have debated what representation should be used to capture the meaning of natural language utterances. Recently new needs of such representations have been raised in research. Here I survey some of the interesting representations suggested to answer for these new needs.
May 6, 2008
The term {\em complexity} is used informally both as a quality and as a quantity. As a quality, complexity has something to do with our ability to understand a system or object -- we understand simple systems, but not complex ones. On another level, {\em complexity} is used as a quantity, when we talk about something being more complicated than another. In this chapter, we explore the formalisation of both meanings of complexity, which happened during the latter half of the...
August 6, 2020
End-to-end spoken language understanding (SLU) models are a class of model architectures that predict semantics directly from speech. Because of their input and output types, we refer to them as speech-to-interpretation (STI) models. Previous works have successfully applied STI models to targeted use cases, such as recognizing home automation commands, however no study has yet addressed how these models generalize to broader use cases. In this work, we analyze the relationshi...