Inhaltsangabe
Scientific Study from the year 2018 in the subject Computer Sciences - Artificial Intelligence, grade: 1, Post Graduate Government College, language: English, abstract: Every natural language contains a large number of words. These words can have different senses in different context; such words with multiple senses are known as sense tagged words. Word sense reflects the basic concept of the word and the words with several meanings cause ambiguity in the sentence, and the process that decides which of the denotation is accurate in the sentence among several meanings of the word is known as Word Sense Disambiguation. Human beings are good at understanding the meaning of the word by reading the sentence but the same task is difficult for a machine: to understand and accurately sense the correct meaning of the word. Machines can easily understand the set of rules and it is a difficult task to create such rules that can easily disambiguate the word in the context. This task is complicated because every natural language has their own set of rules such as grammatical rules, part-of-speech, antonomy, and synonym. Therefore, a machine is trained by special algorithm so that it can tag the word with its correct sense. If the correct sense of the word is determined, that correct sense is helpful in retrieving the basic concepts of the word. As such this is very difficult task for a machine to retrieve the basic definition of word. In this proposed work, K-Nearest Neighbor (KNN) approach is used to disambiguate the sense tagged words. The KNN is based on supervised learning method. The proposed technique evaluates the performance on Hindi sense tagged words and these are obtained from Hindi Wordnet. The results show the effectiveness of the proposed technique in sense tagged words.
Reseña del editor
Scientific Study from the year 2018 in the subject Computer Science - Miscellaneous, grade: 1, Post Graduate Government College, language: English, abstract: Every natural language contains a large number of words. These words can have different senses in different context; such words with multiple senses are known as sense tagged words. Word sense reflects the basic concept of the word and the words with several meanings cause ambiguity in the sentence, and the process that decides which of the denotation is accurate in the sentence among several meanings of the word is known as Word Sense Disambiguation. Human beings are good at understanding the meaning of the word by reading the sentence but the same task is difficult for a machine: to understand and accurately sense the correct meaning of the word. Machines can easily understand the set of rules and it is a difficult task to create such rules that can easily disambiguate the word in the context. This task is complicated because every natural language has their own set of rules such as grammatical rules, part-of-speech, antonomy, and synonym. Therefore, a machine is trained by special algorithm so that it can tag the word with its correct sense. If the correct sense of the word is determined, that correct sense is helpful in retrieving the basic concepts of the word. As such this is very difficult task for a machine to retrieve the basic definition of word. In this proposed work, K-Nearest Neighbor (KNN) approach is used to disambiguate the sense tagged words. The KNN is based on supervised learning method. The proposed technique evaluates the performance on Hindi sense tagged words and these are obtained from Hindi Wordnet. The results show the effectiveness of the proposed technique in sense tagged words.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
