The SENSEVAL-3 task to perform word-sense disambiguation of WordNet glosses was designed to encourage development of technology to make use of standard lexical resources. The task was based on the availability of sense-disambiguated hand-tagged glosses created in the eXtended WordNet project. The hand-tagged glosses provided a “gold standard ” for judging the performance of automated disambiguation systems. Seven teams participated in the task, with a total of 10 runs. Scoring these runs as an “all-words ” task, along with considerable discussions among participants, provided more insights than just the underlying technology. The task identified several issues about the nature of the WordNet sense inventory and the underlying use of wordnet...
This paper introduces SenseLearner - a minimally supervised sense tagger that attempts to disambigua...
This paper describes SENSELEARNER – a minimally supervised word sense disambiguation system that att...
The unavailability of very large corpora with semantically disambiguated words is a major limitation...
This paper describes the TALP systems presented at Senseval-3 task 12 “Word-Sense Disambigua-tion of...
The task of word sense disambiguation is to assign a sense label to a word in a passage. We report o...
For SENSEVAL-2, we disambiguated the lexical sample using two different sense inventories. Official ...
This paper presents the task definition, resources, participating systems, and comparative results f...
This paper presents a new approach for combining differ-ent semantic disambiguation methods that are...
Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a comp...
Abstract: The paper presents a method for word sense disambiguation (WSD) based on parallel corpora....
Abstract. The lack of large, semantically annotated corpora is one of the main drawbacks of Word Sen...
This paper introduces SENSELEARNER – a minimally supervised sense tagger that attempts to disambigua...
An important problem in Natural Language Processing is identifying the correct sense of a word in a ...
This paper presents the Swarthmore College word-sense disambiguation system which was designed for t...
Fine-grained sense distinctions are one of the major obstacles to successful Word Sense Disambiguati...
This paper introduces SenseLearner - a minimally supervised sense tagger that attempts to disambigua...
This paper describes SENSELEARNER – a minimally supervised word sense disambiguation system that att...
The unavailability of very large corpora with semantically disambiguated words is a major limitation...
This paper describes the TALP systems presented at Senseval-3 task 12 “Word-Sense Disambigua-tion of...
The task of word sense disambiguation is to assign a sense label to a word in a passage. We report o...
For SENSEVAL-2, we disambiguated the lexical sample using two different sense inventories. Official ...
This paper presents the task definition, resources, participating systems, and comparative results f...
This paper presents a new approach for combining differ-ent semantic disambiguation methods that are...
Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a comp...
Abstract: The paper presents a method for word sense disambiguation (WSD) based on parallel corpora....
Abstract. The lack of large, semantically annotated corpora is one of the main drawbacks of Word Sen...
This paper introduces SENSELEARNER – a minimally supervised sense tagger that attempts to disambigua...
An important problem in Natural Language Processing is identifying the correct sense of a word in a ...
This paper presents the Swarthmore College word-sense disambiguation system which was designed for t...
Fine-grained sense distinctions are one of the major obstacles to successful Word Sense Disambiguati...
This paper introduces SenseLearner - a minimally supervised sense tagger that attempts to disambigua...
This paper describes SENSELEARNER – a minimally supervised word sense disambiguation system that att...
The unavailability of very large corpora with semantically disambiguated words is a major limitation...