This paper summarizes the results of some experiments for finding the effective features for disambiguation of Turkish verbs. Word sense disambiguation is a current area of investigation in which verbs have the dominant role. Generally verbs have more senses than the other types of words in the average and detecting these features for verbs may lead to some improvements for other word types. In this paper we have considered only the syntactical features that can be obtained from the corpus and tested by using some famous machine learning algorithms
Verbs that can have more than one meaning pose problems for Natural Language Processing (NLP) applic...
This research examines a word sense dis-ambiguation method using selectors acquired from the Web. Se...
Identifying the correct sense of a word in context is crucial for many tasks in natural language pro...
This paper summarizes the results of some experiments for finding the effective features for disambi...
Word Sense Disambiguation (WSD) is the core and one of the hardest problems of many Natural Language...
Feature selection in Word Sense Disambiguation (WSD) is as important as the selection of algorithm t...
Word sense disambiguation is an important intermediate stage for many natural language processing ap...
Semantic analysis has become a bottleneck of many natural language applications. Machine translation...
We describe experiments in Machine Translation using word sense disambiguation (WSD) information. Th...
To create the first Hungarian WSD corpus, 39 suitable word form samples were selected for the purpos...
This research examines a word sense disambiguation method using selectors acquired from theWeb. Sele...
Semantic analysis has become a bottleneck of many natural language applications. Machine translation...
Word Sense Disambiguation (WSD) is an important area which has an impact on improving the performanc...
Present study introduces a machine-based approach for Word Sense Disambiguation (WSD). In Persian, a...
In this paper, we investigated the features which discriminates verbs in Turkish. Though, words in T...
Verbs that can have more than one meaning pose problems for Natural Language Processing (NLP) applic...
This research examines a word sense dis-ambiguation method using selectors acquired from the Web. Se...
Identifying the correct sense of a word in context is crucial for many tasks in natural language pro...
This paper summarizes the results of some experiments for finding the effective features for disambi...
Word Sense Disambiguation (WSD) is the core and one of the hardest problems of many Natural Language...
Feature selection in Word Sense Disambiguation (WSD) is as important as the selection of algorithm t...
Word sense disambiguation is an important intermediate stage for many natural language processing ap...
Semantic analysis has become a bottleneck of many natural language applications. Machine translation...
We describe experiments in Machine Translation using word sense disambiguation (WSD) information. Th...
To create the first Hungarian WSD corpus, 39 suitable word form samples were selected for the purpos...
This research examines a word sense disambiguation method using selectors acquired from theWeb. Sele...
Semantic analysis has become a bottleneck of many natural language applications. Machine translation...
Word Sense Disambiguation (WSD) is an important area which has an impact on improving the performanc...
Present study introduces a machine-based approach for Word Sense Disambiguation (WSD). In Persian, a...
In this paper, we investigated the features which discriminates verbs in Turkish. Though, words in T...
Verbs that can have more than one meaning pose problems for Natural Language Processing (NLP) applic...
This research examines a word sense dis-ambiguation method using selectors acquired from the Web. Se...
Identifying the correct sense of a word in context is crucial for many tasks in natural language pro...