Location recognition with fuzzy grammar
Fuzzy grammar has been introduced as an approach to represent and learn text fragments where the set of learned patterns are represented by combining similar segments to represent regularities and marking interchangeable segments. This paper is dedicated to present a procedural scheme towards learni...
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| Other Authors: | |
| Format: | Book Section |
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Springer
2012
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| _version_ | 1825925904739598336 |
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| author | Mohd Sharef, Nurfadhlina |
| author2 | Lukose, Dickson |
| author_facet | Lukose, Dickson Mohd Sharef, Nurfadhlina |
| author_sort | Mohd Sharef, Nurfadhlina |
| collection | UPM |
| description | Fuzzy grammar has been introduced as an approach to represent and learn text fragments where the set of learned patterns are represented by combining similar segments to represent regularities and marking interchangeable segments. This paper is dedicated to present a procedural scheme towards learning text fragment in text categorization task facilitated by fuzzy grammars. A few issues are involved in developing fuzzy grammars which are (i) determination of the number of text classes to develop (ii) the selection of text fragments, F relevant to each text class (iii) determining frequent and important terms or keywords, V to develop the set of terminal, T and compound grammars, N. (iv) Conversion of text fragments into grammars, (v) Combination of grammars into a compact form. Comparison between fuzzy grammar and other location entity identifier such as LbjTagger, LingPipe, Newswire and Open Calais is observed where results have shown that this method outperforms other standard machine learning and statistical-based approach. |
| first_indexed | 2024-03-06T08:04:38Z |
| format | Book Section |
| id | upm.eprints-26092 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T08:04:38Z |
| publishDate | 2012 |
| publisher | Springer |
| record_format | dspace |
| spelling | upm.eprints-260922016-01-19T04:26:06Z http://psasir.upm.edu.my/id/eprint/26092/ Location recognition with fuzzy grammar Mohd Sharef, Nurfadhlina Fuzzy grammar has been introduced as an approach to represent and learn text fragments where the set of learned patterns are represented by combining similar segments to represent regularities and marking interchangeable segments. This paper is dedicated to present a procedural scheme towards learning text fragment in text categorization task facilitated by fuzzy grammars. A few issues are involved in developing fuzzy grammars which are (i) determination of the number of text classes to develop (ii) the selection of text fragments, F relevant to each text class (iii) determining frequent and important terms or keywords, V to develop the set of terminal, T and compound grammars, N. (iv) Conversion of text fragments into grammars, (v) Combination of grammars into a compact form. Comparison between fuzzy grammar and other location entity identifier such as LbjTagger, LingPipe, Newswire and Open Calais is observed where results have shown that this method outperforms other standard machine learning and statistical-based approach. Springer Lukose, Dickson Ahmad, Abdul Rahim Suliman , Azizah 2012 Book Section PeerReviewed Mohd Sharef, Nurfadhlina (2012) Location recognition with fuzzy grammar. In: Knowledge Technology. Communications in Computer and Information Science (295). Springer, Berlin, pp. 254-261. ISBN 9783642328251; EISBN: 9783642328268 10.1007/978-3-642-32826-8_26 |
| spellingShingle | Mohd Sharef, Nurfadhlina Location recognition with fuzzy grammar |
| title | Location recognition with fuzzy grammar |
| title_full | Location recognition with fuzzy grammar |
| title_fullStr | Location recognition with fuzzy grammar |
| title_full_unstemmed | Location recognition with fuzzy grammar |
| title_short | Location recognition with fuzzy grammar |
| title_sort | location recognition with fuzzy grammar |
| work_keys_str_mv | AT mohdsharefnurfadhlina locationrecognitionwithfuzzygrammar |