Papers › Named entity recognition architecture combining contextual and global features

Named entity recognition architecture combining contextual and global features

15 Dec 2021arXiv:2112.08033archive 2025-07-28

Tran Thi Hong Hanh, Antoine Doucet, Nicolas Sidere, Jose G. Moreno, Senja Pollak

Named entity recognition (NER) is an information extraction technique that aims to locate and classify named entities (e.g., organizations, locations,...) within a document into predefined categories. Correctly identifying these phrases plays a significant role in simplifying information access. However, it remains a difficult task because named entities (NEs) have multiple forms and they are context-dependent. While the context can be represented by contextual features, global relations are often misrepresented by those models. In this paper, we propose the combination of contextual features from XLNet and global features from Graph Convolution Network (GCN) to enhance NER performance. Experiments over a widely-used dataset, CoNLL 2003, show the benefits of our strategy, with results competitive with the state of the art (SOTA).

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Tasks

NERNamed Entity RecognitionNamed Entity Recognition (NER)named-entity-recognition

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Named Entity Recognition (NER) CoNLL 2003 (English) XLNet-GCN F1 93.82 #9 of 73 Archive leaderboard report
Named Entity Recognition (NER) CoNLL 2003 (English) XLNet F1 93.28 #27 of 73 Archive leaderboard report
Named Entity Recognition (NER) CoNLL 2003 (English) GCN F1 88.63 #71 of 73 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.

Methods

AdamAttentionBPEConvolutionDense ConnectionsDropoutLayer NormalizationLinear LayerLinear Warmup With Linear DecayMulti-Head AttentionResidual ConnectionSentencePieceSoftmaxXLNet

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