Abstract not found

The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal

Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused.

This article presents a theory in which automatization is construed as the acquisition of a domain-specific knowledge base, formed of separate representations, instances, of each exposure to the task. Processing is considered automatic if it relies on retrieval of stored instances, which will occur

is automatic and unsupervised in both training and application: senses are induced from a corpus without labeled training insta,nces or other external knowledge sources. The paper demonstrates good performance of context-group discrimination for a sample of natural and artificial ambiguous words

We present a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame. Various lexical and syntactic features are derived from parse trees and used to derive statistical classifiers from hand-annotated training data. 1

A two-process theory of human information processing is proposed and applied to detection, search, and attention phenomena. Automatic processing is activa-tion of a learned sequence of elements in long-term memory that is initiated by appropriate inputs and then proceeds automatically

We describe a method for the automatic acquisition of the hyponymy lexical relation from unrestricted text. Two goals motivate the approach: (i) avoidante of the need for pre-encoded knowledge and (ii) applicability across a wide range of text. We identify a set of lexico-syntactic patterns

crucially on the choice of effective term-weighting systems. This article summarizes the insights gained in automatic term weighting, and provides baseline single-term-indexing models with which other more elaborate content analysis procedures can be compared.

by statistical properties related to the instrumentation, rhythmic structure and form of its members. In this work, algorithms for the automatic genre categorization of audio signals are described. More specifically, we propose a set of features for representing texture and instrumentation. In addition a novel