Semantically factoid question answering using fuzzy SVM named entity recognition
Named Entity Recognition (NER) and Question Answering (QA) are fundamental tasks and they are the cores of natural language processing (NLP) system. NER, a sub problem of Information Extraction (IE), involves recognizing and extracting name entities like Persons, Locations, Organizations, Dates and...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2008
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| Online Access: | http://psasir.upm.edu.my/id/eprint/68864/ http://psasir.upm.edu.my/id/eprint/68864/1/Semantically%20factoid%20question%20answering%20using%20fuzzy%20SVM%20named%20entity%20recognition.pdf |
| _version_ | 1848856247510499328 |
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| author | Mansouri, Alireza Affendey, Lilly Suriani Mamat, Ali Abdul Kadir, Rabiah |
| author_facet | Mansouri, Alireza Affendey, Lilly Suriani Mamat, Ali Abdul Kadir, Rabiah |
| author_sort | Mansouri, Alireza |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Named Entity Recognition (NER) and Question Answering (QA) are fundamental tasks and they are the cores of natural language processing (NLP) system. NER, a sub problem of Information Extraction (IE), involves recognizing and extracting name entities like Persons, Locations, Organizations, Dates and Times from electronics resources and text. Question Answering (QA) is a type of Information Retrieval (IR), attempts to deal with a wide range of question. In this paper we propose a semantically Factoid Question Answering model using Fuzzy Support Vector Machine Named Entity Recognizer component called FSVM. In this model we applied the FSVM NE recognizer to filter Question Answering system results have token by IR and return exact expect result to the user. This paper shows how the Fuzzy NER can applied in information retrieval (IR) systems in applications like Question Answering (QA). We show a model to improve precision in QA by semantically NER and reducing Answer Finder input data. |
| first_indexed | 2025-11-15T11:38:37Z |
| format | Conference or Workshop Item |
| id | upm-68864 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:38:37Z |
| publishDate | 2008 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-688642019-06-11T01:41:32Z http://psasir.upm.edu.my/id/eprint/68864/ Semantically factoid question answering using fuzzy SVM named entity recognition Mansouri, Alireza Affendey, Lilly Suriani Mamat, Ali Abdul Kadir, Rabiah Named Entity Recognition (NER) and Question Answering (QA) are fundamental tasks and they are the cores of natural language processing (NLP) system. NER, a sub problem of Information Extraction (IE), involves recognizing and extracting name entities like Persons, Locations, Organizations, Dates and Times from electronics resources and text. Question Answering (QA) is a type of Information Retrieval (IR), attempts to deal with a wide range of question. In this paper we propose a semantically Factoid Question Answering model using Fuzzy Support Vector Machine Named Entity Recognizer component called FSVM. In this model we applied the FSVM NE recognizer to filter Question Answering system results have token by IR and return exact expect result to the user. This paper shows how the Fuzzy NER can applied in information retrieval (IR) systems in applications like Question Answering (QA). We show a model to improve precision in QA by semantically NER and reducing Answer Finder input data. IEEE 2008 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/68864/1/Semantically%20factoid%20question%20answering%20using%20fuzzy%20SVM%20named%20entity%20recognition.pdf Mansouri, Alireza and Affendey, Lilly Suriani and Mamat, Ali and Abdul Kadir, Rabiah (2008) Semantically factoid question answering using fuzzy SVM named entity recognition. In: 3rd International Symposium on Information Technology (ITSim'08), 26-28 Aug. 2008, Kuala Lumpur, Malaysia. . 10.1109/ITSIM.2008.4631684 |
| spellingShingle | Mansouri, Alireza Affendey, Lilly Suriani Mamat, Ali Abdul Kadir, Rabiah Semantically factoid question answering using fuzzy SVM named entity recognition |
| title | Semantically factoid question answering using fuzzy SVM named entity recognition |
| title_full | Semantically factoid question answering using fuzzy SVM named entity recognition |
| title_fullStr | Semantically factoid question answering using fuzzy SVM named entity recognition |
| title_full_unstemmed | Semantically factoid question answering using fuzzy SVM named entity recognition |
| title_short | Semantically factoid question answering using fuzzy SVM named entity recognition |
| title_sort | semantically factoid question answering using fuzzy svm named entity recognition |
| url | http://psasir.upm.edu.my/id/eprint/68864/ http://psasir.upm.edu.my/id/eprint/68864/ http://psasir.upm.edu.my/id/eprint/68864/1/Semantically%20factoid%20question%20answering%20using%20fuzzy%20SVM%20named%20entity%20recognition.pdf |