Add to ORKGThis paper describes a lexical organization in which "senses " are represented in their own right, along with "words " and "phrases", by distinct data items. The objective of the scheme is to facilitate recognition and employment of synonyms and stock phrases by programs which process natural language. Besides presenting the proposed organization, the paper characterizes the lexical "senses " which result. Keywords
This paper describes a system, Wolfie (WOrd Learning From Interpreted Examples), that acquires a sem...
Accurate semantic modeling lies at the very core of today’s Natural Language Processing (NLP). Getti...
Word Sense Disambiguation (WSD) and Word Sense Induction (WSI) are two fundamental tasks in Natural ...
A user expresses her information need through words with a precise meaning, but from the machine poi...
Most natural language processing tasks require lexical semantic information. Automated acquisition o...
The aim of this study was to determine whether some of the approaches of lexical semantics for study...
This paper describes several aspects of the process of lexical acquisition for one of the most compr...
The representation of written language semantics is a central problem of language technology and a c...
Lexicon coverage is often the limiting factor in natural language processing systems. Recent work ha...
Lexical resources are important components of natural language processing (NLP) applications providi...
In this paper, we examine how lexical knowledge has been modelled and used in generation systems of ...
The lexical level of natural language processing focuses on the notion of word. This survey will be ...
A system tbr lexical acquisition is presented where word meanings are represented by clusters of phr...
An important problem in Natural Language Processing is identifying the correct sense of a word in a ...
Progress is being made in Natural Language Processing (NLP) but there is still a long way towards Na...
This paper describes a system, Wolfie (WOrd Learning From Interpreted Examples), that acquires a sem...
Accurate semantic modeling lies at the very core of today’s Natural Language Processing (NLP). Getti...
Word Sense Disambiguation (WSD) and Word Sense Induction (WSI) are two fundamental tasks in Natural ...
A user expresses her information need through words with a precise meaning, but from the machine poi...
Most natural language processing tasks require lexical semantic information. Automated acquisition o...
The aim of this study was to determine whether some of the approaches of lexical semantics for study...
This paper describes several aspects of the process of lexical acquisition for one of the most compr...
The representation of written language semantics is a central problem of language technology and a c...
Lexicon coverage is often the limiting factor in natural language processing systems. Recent work ha...
Lexical resources are important components of natural language processing (NLP) applications providi...
In this paper, we examine how lexical knowledge has been modelled and used in generation systems of ...
The lexical level of natural language processing focuses on the notion of word. This survey will be ...
A system tbr lexical acquisition is presented where word meanings are represented by clusters of phr...
An important problem in Natural Language Processing is identifying the correct sense of a word in a ...
Progress is being made in Natural Language Processing (NLP) but there is still a long way towards Na...
This paper describes a system, Wolfie (WOrd Learning From Interpreted Examples), that acquires a sem...
Accurate semantic modeling lies at the very core of today’s Natural Language Processing (NLP). Getti...
Word Sense Disambiguation (WSD) and Word Sense Induction (WSI) are two fundamental tasks in Natural ...