Abstract:
Word-sense disambiguation is one of the key concepts in natural language processing. The main goal of a language is to present a specific concept to the audience. This co...Show MoreMetadata
Abstract:
Word-sense disambiguation is one of the key concepts in natural language processing. The main goal of a language is to present a specific concept to the audience. This concept is extracted from the meaning of words in that language. System should be able to identify role and meaning of words in order to identify the concepts in texts properly. This issue becomes more problematic if there are words that take different meanings because of their surrounding words. Regarding that different practical programs have been developed in Persian language, it is vital now to find a solution for word-sense disambiguation in Persian language. Lack of training data is the biggest challenge in the course of word-sense disambiguation in Persian language. In order to face this problem, machine learning approach with minimal supervision is employed in this research. The applied method tries to disambiguate word senses by considering defined features of target words and applying collaborative learning method. Extracted corpus from published news by news agencies is used as the reference corpus. Evaluating the program by the available corpus on three considered ambiguous words, the implemented method has been able to properly identify the meaning of 5368 documents with 88% recall, 95% precision and 93% accuracy rate.
Date of Conference: 26-27 October 2017
Date Added to IEEE Xplore: 07 December 2017
ISBN Information:
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- IEEE Keywords
- Index Terms
- Word Sense Disambiguation ,
- Machine Learning ,
- Training Data ,
- Implementation Of Method ,
- Word Meaning ,
- Target Word ,
- Words In Language ,
- Ambiguous Words ,
- Lack Of Training Data ,
- Persian Language ,
- Raw Data ,
- Supervised Learning ,
- Computational Load ,
- Part-of-speech ,
- Technological Knowledge ,
- Text Words ,
- Data Sharing ,
- Words In Sentences ,
- Feature Matching ,
- Semi-supervised Methods ,
- Equal Sections ,
- Semi-supervised Learning ,
- Semantic Labels ,
- Supervised Learning Methods ,
- Google Search Engine ,
- Holdout Dataset ,
- Polysemy ,
- Suitable Dataset ,
- Correct Meaning
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Word Sense Disambiguation ,
- Machine Learning ,
- Training Data ,
- Implementation Of Method ,
- Word Meaning ,
- Target Word ,
- Words In Language ,
- Ambiguous Words ,
- Lack Of Training Data ,
- Persian Language ,
- Raw Data ,
- Supervised Learning ,
- Computational Load ,
- Part-of-speech ,
- Technological Knowledge ,
- Text Words ,
- Data Sharing ,
- Words In Sentences ,
- Feature Matching ,
- Semi-supervised Methods ,
- Equal Sections ,
- Semi-supervised Learning ,
- Semantic Labels ,
- Supervised Learning Methods ,
- Google Search Engine ,
- Holdout Dataset ,
- Polysemy ,
- Suitable Dataset ,
- Correct Meaning
- Author Keywords