Nested Named Entity Recognition Revisited

Katiyar, Arzoo; Cardie, Claire

doi:10.18653/v1/n18-1079

Cited by 191 publications

(206 citation statements)

References 16 publications

(38 reference statements)

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“…In nested named entity recognition, entities can be overlapping and labeled with more than one label such as in the example "The Florida Supreme Court" containing two overlapping named entities "The Florida Supreme Court" and "Florida". 1 Recent publications on nested named entity recognition involve stacked LSTM-CRF NE recognizer (Ju et al, 2018), or a construction of a special structure that explicitly captures the nested entities, such as a constituency graph (Finkel and Manning, 2009) or various modifications of a directed hypergraph (Lu and Roth, 2015;Katiyar and Cardie, 2018;Wang and Lu, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Neural Architectures for Nested NER through Linearization

Straková¹,

Straka²,

Hajič³

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

205

131

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We propose two neural network architectures for nested named entity recognition (NER), a setting in which named entities may overlap and also be labeled with more than one label. We encode the nested labels using a linearized scheme. In our first proposed approach, the nested labels are modeled as multilabels corresponding to the Cartesian product of the nested labels in a standard LSTM-CRF architecture. In the second one, the nested NER is viewed as a sequence-to-sequence problem, in which the input sequence consists of the tokens and output sequence of the labels, using hard attention on the word whose label is being predicted. The proposed methods outperform the nested NER state of the art on four corpora: ACE-2004, ACE-2005, GENIA and Czech CNEC. We also enrich our architectures with the recently published contextual embeddings: ELMo, BERT and Flair, reaching further improvements for the four nested entity corpora. In addition, we report flat NER stateof-the-art results for CoNLL-2002 Dutch and Spanish and for CoNLL-2003 English.

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Section: Introductionmentioning

confidence: 99%

“…Wang and Lu (2018) build a hypergraph to capture all possible entity mentions in a sentence. Katiyar and Cardie (2018) model nested entities as a directed hypergraph similar to Lu and Roth (2015), using RNNs to model the edge probabilities.…”

Section: Introductionmentioning

confidence: 99%

Neural Architectures for Nested NER through Linearization

Straková¹,

Straka²,

Hajič³

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

205

131

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“…The proposed MGNER is very suitable for detecting nested named entities since every possible entity will be examined and classified. In order to validate this advantage, we compare MGNER with numerous baseline models: 1) Lu and Roth (2015) which propose the mention hypergraphs for recognizing overlapping entities; 2) Lample et al (2016) which adopt the LSTM-CRF stucture for sequence labelling; 3) Muis and Lu (2017) which introduce mention separators to tag gaps between words for recognizing overlapping mentions; 4) Xu et al (2017) that propose a local detection method; 5) Katiyar and Cardie (2018) which propose a hypergraph-based model using LSTM for learning feature representations; 6) Ju et al (2018) that use a layered model which extracts outer entities based on inner ones; 7) which propose a neural transition-based model that constructs nested mentions through a sequence of actions; 8) which adopt a neural segmental hypergraph model. Experiment results of the Nested NER task on the ACE-2004 andACE-2005 datasets are reported in Table 2.…”

Section: Resultsmentioning

confidence: 99%

Multi-grained Named Entity Recognition

Xia¹,

Zhang²,

Yang³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be nonoverlapping or totally nested. Different from traditional approaches regarding NER as a sequential labeling task and annotate entities consecutively, MGNER detects and recognizes entities on multiple granularities: it is able to recognize named entities without explicitly assuming non-overlapping or totally nested structures. MGNER consists of a Detector that examines all possible word segments and a Classifier that categorizes entities. In addition, contextual information and a self-attention mechanism are utilized throughout the framework to improve the NER performance. Experimental results show that MGNER outperforms current state-of-the-art baselines up to 4.4% in terms of the F1 score among nested/non-overlapping NER tasks.

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“…Unlike methods such as Katiyar and Cardie (2018), it does not predict entity segmentation at each layer as discrete 0-1 labels, thus allowing the model to flexibly aggregate information across layers. Furthermore inference is greedy, without attempting to score all possible entity spans as in , which results in faster decoding (decoding requires simply a single forward pass of the network).…”

Section: Introductionmentioning

confidence: 99%

Merge and Label: A Novel Neural Network Architecture for Nested NER

Fisher¹,

Vlachos²

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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Named entity recognition (NER) is one of the best studied tasks in natural language processing. However, most approaches are not capable of handling nested structures which are common in many applications. In this paper we introduce a novel neural network architecture that first merges tokens and/or entities into entities forming nested structures, and then labels each of them independently. Unlike previous work, our merge and label approach predicts real-valued instead of discrete segmentation structures, which allow it to combine word and nested entity embeddings while maintaining differentiability. We evaluate our approach using the ACE 2005 Corpus, where it achieves state-of-the-art F1 of 74.6, further improved with contextual embeddings (BERT) to 82.4, an overall improvement of close to 8 F1 points over previous approaches trained on the same data. Additionally we compare it against BiLSTM-CRFs, the dominant approach for flat NER structures, demonstrating that its ability to predict nested structures does not impact performance in simpler cases. 1

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Nested Named Entity Recognition Revisited

Cited by 191 publications

References 16 publications

Neural Architectures for Nested NER through Linearization

Neural Architectures for Nested NER through Linearization

Multi-grained Named Entity Recognition

Merge and Label: A Novel Neural Network Architecture for Nested NER

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