Abstract — Natural language processing systems are increasingly integrating lexicons with ontologies for word sense disambiguation (WSD). Manually acquiring a lexicon that is integrated with a large ontology and other semantic resources can be difficult and inefficient in part due to the complexity of ontologies and inconsistency of entity extractors supporting WSD applications. A major contributing factor to the difficulty is the creation of selectional restrictions with respect to particular semantic resources. This paper presents a process for acquiring complex expressions for selectional restrictions via search through an ontology. I
Abstract: The paper presents a method for word sense disambiguation (WSD) based on parallel corpora....
Understanding the user's intention is crucial for many tasks that involve human-machine interaction....
Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowled...
First, we propose a unified framework for evaluating verb sense in a selectional restriction-based d...
We present results that show that incorporating lexical and structural semantic information is effec...
Verbs that can have more than one meaning pose problems for Natural Language Processing (NLP) applic...
We present results that show that incorporating lexical and structural semantic information is effec...
Word Sense Disambiguation (WSD) is traditionally considered an AI-hard problem. A break-through in t...
Natural language is inherently ambiguous. For example, the word "bank" can mean a financial institut...
The unavailability of very large corpora with semantically disambiguated words is a major limitation...
Selectional preferences are a source of linguistic information commonly applied to the task of Word ...
Word Sense Disambiguation (WSD) is an important but challenging technique in the area of natural lan...
The selectional preferences of verbal predicates are an important component of a computational lexic...
An important problem in Natural Language Processing is identifying the correct sense of a word in a ...
The selectional preferences of verbal predicates are an importantcomponent of a computational lexico...
Abstract: The paper presents a method for word sense disambiguation (WSD) based on parallel corpora....
Understanding the user's intention is crucial for many tasks that involve human-machine interaction....
Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowled...
First, we propose a unified framework for evaluating verb sense in a selectional restriction-based d...
We present results that show that incorporating lexical and structural semantic information is effec...
Verbs that can have more than one meaning pose problems for Natural Language Processing (NLP) applic...
We present results that show that incorporating lexical and structural semantic information is effec...
Word Sense Disambiguation (WSD) is traditionally considered an AI-hard problem. A break-through in t...
Natural language is inherently ambiguous. For example, the word "bank" can mean a financial institut...
The unavailability of very large corpora with semantically disambiguated words is a major limitation...
Selectional preferences are a source of linguistic information commonly applied to the task of Word ...
Word Sense Disambiguation (WSD) is an important but challenging technique in the area of natural lan...
The selectional preferences of verbal predicates are an important component of a computational lexic...
An important problem in Natural Language Processing is identifying the correct sense of a word in a ...
The selectional preferences of verbal predicates are an importantcomponent of a computational lexico...
Abstract: The paper presents a method for word sense disambiguation (WSD) based on parallel corpora....
Understanding the user's intention is crucial for many tasks that involve human-machine interaction....
Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowled...