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| 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 | |a1456417238 |
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| 039 | |a20241202144550|bidtocn|c20200701082809|dthuvt|y20191127150010|zthuvt |
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| 041 | 0 |avie |
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| 044 | |avm |
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| 100 | 0 |aPhạm, Quang Nhật Minh |
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| 245 | 10|aA Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Evaluation Campaign / |cPhạm Quang Nhật Minh |
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| 260 | |c2018. |
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| 300 | 10|atr.311-321 |
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| 650 | 10|aIn 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 |aĐánh giá |
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| 653 | 0 |aNested named-entity recognition |
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| 653 | 0 |aCRF |
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| 653 | 0 |aVLSP |
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| 653 | 0 |aNhận dạng thực thể |
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| 773 | 0 |tTạp chí Tin học và Điều khiển học|gVol.34, No 4 |
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| 890 | |a0|b0|c0|d0 |
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