Gathering Statistics to Aspectually Classify Sentences with a Genetic Algorithm

This paper presents a method for large corpus analysis to semantically classify an entire clause. In particular, we use cooccurrence statistics among similar clauses to determine the aspectual class of an input clause. The process examines linguistic features of clauses that are relevant to aspectual classification. A genetic algorithm determines what combinations of linguistic features to use for this task.Comment: postscript, 9 pages, Proceedings of the Second International Conference on New Methods in Language Processing, Oflazer and Somers ed

A discriminative approach to grounded spoken language understanding in interactive robotics

Spoken Language Understanding in Interactive Robotics provides computational models of human-machine communication based on the vocal input. However, robots operate in specific environments and the correct interpretation of the spoken sentences depends on the physical, cognitive and linguistic aspects triggered by the operational environment. Grounded language processing should exploit both the physical constraints of the context as well as knowledge assumptions of the robot. These include the subjective perception of the environment that explicitly affects linguistic reasoning. In this work, a standard linguistic pipeline for semantic parsing is extended toward a form of perceptually informed natural language processing that combines discriminative learning and distributional semantics. Empirical results achieve up to a 40% of relative error reduction

COGENT programming manual

COGENT /COmpiler and GENeralized Translator/ programming system is a compiler whose input language enables a description of symbolic and linguistic manipulation algorithms. Primarily for use as a compiler-compiler, it is also applicable to algebraic manipulation, mechanical theorem proving, and heuristic programming

The Language of Bias: A Linguistic Approach to Understanding Intergroup Relations

[Excerpt] This chapter explores the role of language in the relationship between diversity and team performance. Specifically, we consider how a linguistic approach to social categorization may be used to study the social psychological mechanisms that underlie diversity effects. Using the results of a study examining the effects of gender, ethnicity and tenure on language abstraction, we consider the potential implications for team processes and effectiveness. In addition, we propose a revised team input-process-output model that highlights the potential effects of language on team processes. We conclude by suggesting directions for future research linking diversity, linguistic categorization and team effectiveness

Adaptive Non-singleton Type-2 Fuzzy Logic Systems: A Way Forward for Handling Numerical Uncertainties in Real World Applications

Real world environments are characterized by high levels of linguistic and numerical uncertainties. A Fuzzy Logic System (FLS) is recognized as an adequate methodology to handle the uncertainties and imprecision available in real world environments and applications. Since the invention of fuzzy logic, it has been applied with great success to numerous real world applications such as washing machines, food processors, battery chargers, electrical vehicles, and several other domestic and industrial appliances. The first generation of FLSs were type-1 FLSs in which type-1 fuzzy sets were employed. Later, it was found that using type-2 FLSs can enable the handling of higher levels of uncertainties. Recent works have shown that interval type-2 FLSs can outperform type-1 FLSs in the applications which encompass high uncertainty levels. However, the majority of interval type-2 FLSs handle the linguistic and input numerical uncertainties using singleton interval type-2 FLSs that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. This ignores the fact that if input numerical uncertainties were present, they should affect the incoming inputs to the FLS. Even in the papers that employed non-singleton type-2 FLSs, the input signals were assumed to have a predefined shape (mostly Gaussian or triangular) which might not reflect the real uncertainty distribution which can vary with the associated measurement. In this paper, we will present a new approach which is based on an adaptive non-singleton interval type-2 FLS where the numerical uncertainties will be modeled and handled by non-singleton type-2 fuzzy inputs and the linguistic uncertainties will be handled by interval type-2 fuzzy sets to represent the antecedents’ linguistic labels. The non-singleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the distribution associated with the given sensor. We will present several real world experiments using a real world robot which will show how the proposed type-2 non-singleton type-2 FLS will produce a superior performance to its singleton type-1 and type-2 counterparts when encountering high levels of uncertainties.</jats:p

Three Approaches to Generating Texts in Different Styles

Natural Language Generation (nlg) systems generate texts in English and other human languages from non-linguistic input data. Usually there are a large number of possible texts that can communicate the input data, and nlg systems must choose one of these. We argue that style can be used by nlg systems to choose between possible texts, and explore how this can be done by (1) explicit stylistic parameters, (2) imitating a genre style, and (3) imitating an individual’s style