Papers › Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information
Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information
Yuyang Nie, Yuanhe Tian, Yan Song, Xiang Ao, Xiang Wan
Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the running text. To model such properties, one could rely on existing resources to providing helpful knowledge to the NER task; some existing studies proved the effectiveness of doing so, and yet are limited in appropriately leveraging the knowledge such as distinguishing the important ones for particular context. In this paper, we improve NER by leveraging different types of syntactic information through attentive ensemble, which functionalizes by the proposed key-value memory networks, syntax attention, and the gate mechanism for encoding, weighting and aggregating such syntactic information, respectively. Experimental results on six English and Chinese benchmark datasets suggest the effectiveness of the proposed model and show that it outperforms previous studies on all experiment datasets.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Chinese Named Entity Recognition | OntoNotes 4 | AESINER | F1 | 81.18 | #7 of 15 | Archive leaderboard | report |
| Chinese Named Entity Recognition | Resume NER | AESINER | F1 | 96.62 | #4 of 13 | Archive leaderboard | report |
| Chinese Named Entity Recognition | Weibo NER | AESINER | F1 | 69.78 | #6 of 18 | Archive leaderboard | report |
| Named Entity Recognition (NER) | Ontonotes v5 (English) | AESINER | F1 | 90.32 | #12 of 28 | Archive leaderboard | report |
| Named Entity Recognition (NER) | WNUT 2016 | AESINER | F1 | 55.14 | #3 of 7 | Archive leaderboard | report |
| Named Entity Recognition (NER) | WNUT 2017 | AESINER | F1 | 50.68 | #13 of 23 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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