Papers › Unified Named Entity Recognition as Word-Word Relation Classification
Unified Named Entity Recognition as Word-Word Relation Classification
Jingye Li, Hao Fei, Jiang Liu, Shengqiong Wu, Meishan Zhang, Chong Teng, Donghong Ji, Fei Li
So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka. nested), and discontinuous NER, which have mostly been studied individually. Recently, a growing interest has been built for unified NER, tackling the above three jobs concurrently with one single model. Current best-performing methods mainly include span-based and sequence-to-sequence models, where unfortunately the former merely focus on boundary identification and the latter may suffer from exposure bias. In this work, we present a novel alternative by modeling the unified NER as word-word relation classification, namely W^2NER. The architecture resolves the kernel bottleneck of unified NER by effectively modeling the neighboring relations between entity words with Next-Neighboring-Word (NNW) and Tail-Head-Word-* (THW-*) relations. Based on the W^2NER scheme we develop a neural framework, in which the unified NER is modeled as a 2D grid of word pairs. We then propose multi-granularity 2D convolutions for better refining the grid representations. Finally, a co-predictor is used to sufficiently reason the word-word relations. We perform extensive experiments on 14 widely-used benchmark datasets for flat, overlapped, and discontinuous NER (8 English and 6 Chinese datasets), where our model beats all the current top-performing baselines, pushing the state-of-the-art performances of unified NER.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Chinese Named Entity Recognition | MSRA | W2NER | F1 | 96.10 | #4 of 21 | Archive leaderboard | report |
| Chinese Named Entity Recognition | OntoNotes 4 | W2NER | F1 | 83.08 | #2 of 15 | Archive leaderboard | report |
| Named Entity Recognition (NER) | CoNLL 2003 (English) | W2NER | F1 | 93.07 | #32 of 73 | Archive leaderboard | report |
| Named Entity Recognition (NER) | Ontonotes v5 (English) | W2NER | F1 | 90.50 | #10 of 28 | Archive leaderboard | report |
| Nested Named Entity Recognition | ACE 2004 | W2NER | F1 | 87.52 | #10 of 24 | Archive leaderboard | report |
| Nested Named Entity Recognition | ACE 2005 | W2NER | F1 | 86.79 | #8 of 25 | Archive leaderboard | report |
| Nested Named Entity Recognition | GENIA | W2NER | F1 | 81.39 | #4 of 26 | 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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