| 000 | 00000nab#a2200000ui#4500 |
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| 001 | 57084 |
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| 002 | 2 |
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| 004 | 068FAB9C-4BBB-4046-BAB1-7374634A689D |
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| 005 | 202007010828 |
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| 008 | 081223s2018 vm| vie |
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| 009 | 1 0 |
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| 035 | [ ] |a 1456417238 |
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| 039 | [ ] |a 20241202144550 |b idtocn |c 20200701082809 |d thuvt |y 20191127150010 |z thuvt |
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| 041 | [0 ] |a vie |
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| 044 | [ ] |a vm |
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| 100 | [0 ] |a Phạm, Quang Nhật Minh |
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| 245 | [1 0] |a A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Evaluation Campaign / |c Phạm Quang Nhật Minh |
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| 260 | [ ] |c 2018. |
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| 300 | [1 0] |a tr.311-321 |
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| 650 | [1 0] |a In this report, we describe our participant named-entity recognition system at VLSP 2018 evaluation campaign. We formalized the task as a sequence labeling problem using BIO encoding scheme. We applied a feature-based model which combines word, word-shape features, Brown-cluster-based features, and word-embedding-based features. We compare several methods to deal with nested entities in the dataset. We showed that combining tags of entities at all levels for training a sequence labeling model (joint-tag model) improved the accuracy of nested named-entity recognition. |
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| 653 | [0 ] |a Đánh giá |
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| 653 | [0 ] |a Nested named-entity recognition |
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| 653 | [0 ] |a CRF |
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| 653 | [0 ] |a VLSP |
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| 653 | [0 ] |a Nhận dạng thực thể |
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| 773 | [0 ] |t Tạp chí Tin học và Điều khiển học |g Vol.34, No 4 |
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| 890 | [ ] |a 0 |b 0 |c 0 |d 0 |
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