Create your own natural language training corpus for machine learning. Whether you're
working with English, Chinese, or any other natural language, this hands-on book guides …

This paper briefly surveys three of the main resources for word sense disambiguation that
are currently in use–WordNet, FrameNet, and Levin classes–and proposes an alternative …

The distributional properties extracted from linguistic corpora for a word are regarded by
many as the principle contribution to its meaning. While largely sympathetic to this view, we …

We present the first large-scale, corpus based verification of Dowty's seminal theory of proto-
roles. Our results demonstrate both the need for and the feasibility of a property-based …

When a word sense disambiguation (WSD) system is trained on one domain but applied to a
different domain, a drop in accuracy is frequently observed. This highlights the importance of …

This article gives an overview of how sentence meaning is represented in eleven deep-
syntactic frameworks, ranging from those based on linguistic theories elaborated for …

I start with a brief summary of AS Hornby's achievement in creating the Idiomatic and
Syntactic English Dictionary (ISED 1942), a work which gradually mutated, through many …

This paper proposes that meanings in text are both created and understood by matching
actual text occurrences (or creations) against patterns of usage stored in the brain. A …

It is common practice in computational linguistics to attempt to use selectional constraints
and semantic type hierarchies as primary knowledge resources to perform word sense …

TAGH is a system for automatic recognition of German word forms. It is based on a stem
lexicon with allomorphs and a concatenative mechanism for inflection and word formation …