Hierarchical Nested Named Entity Recognition

Marinho, Zita; Mendes, Afonso; Miranda, Sebastião; Nogueira, David Silva

doi:10.18653/v1/w19-1904

Cited by 11 publications

(12 citation statements)

References 30 publications

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“…Besides, our annotated corpus has hierarchical entities (for example, test result and sign/symptom are part of the entity finding). We plan to investigate the hierarchical nested NER using architectures as in Marinho et al (Marinho et al, 2019). Finally, our corpus has attributes and relations which we have not addressed yet.…”

Section: Discussionmentioning

confidence: 99%

The Chilean Waiting List Corpus: a new resource for clinical Named Entity Recognition in Spanish

Báez

Villena

Rojas³

et al. 2020

Proceedings of the 3rd Clinical Natural Language Processing Workshop

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In this work we describe the Waiting List Corpus consisting of de-identified referrals for several specialty consultations from the waiting list in Chilean public hospitals. A subset of 900 referrals was manually annotated with 9,029 entities, 385 attributes, and 284 pairs of relations with clinical relevance. A trained medical doctor annotated these referrals, and then together with other three researchers, consolidated each of the annotations. The annotated corpus has nested entities, with 32.2% of entities embedded in other entities. We use this annotated corpus to obtain preliminary results for Named Entity Recognition (NER). The best results were achieved by using a biLSTM-CRF architecture using word embeddings trained over Spanish Wikipedia together with clinical embeddings computed by the group. NER models applied to this corpus can leverage statistics of diseases and pending procedures within this waiting list. This work constitutes the first annotated corpus using clinical narratives from Chile, and one of the few for the Spanish language. The annotated corpus, the clinical word embeddings, and the annotation guidelines are freely released to the research community.

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Section: Discussionmentioning

confidence: 99%

The Chilean Waiting List Corpus: a new resource for clinical Named Entity Recognition in Spanish

Báez

Villena

Rojas³

et al. 2020

Proceedings of the 3rd Clinical Natural Language Processing Workshop

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“…[31] employs a set of three actions types (SHIFT, REDUCE-X, UNARY-X) that build a forest of binary word trees. [18] adds a new action (OUT) to allow building multiple mentions for a given span, especially fine-grained entity classes according to a predefined semantic hierarchy.…”

Section: Exhaustive or Semi-exhaustive Methodsmentioning

confidence: 99%

Effect of depth order on iterative nested named entity recognition models

Wajsburt,

Taillé,

Tannier

2021

Preprint

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This paper studies the effect of the order of depth of mention on nested named entity recognition (NER) models. NER is an essential task in the extraction of biomedical information, and nested entities are common since medical concepts can assemble to form larger entities. Conventional NER systems only predict disjointed entities. Thus, iterative models for nested NER use multiple predictions to enumerate all entities, imposing a predefined order from largest to smallest or smallest to largest. We design an order-agnostic iterative model and a procedure to choose a custom order during training and prediction. To accommodate for this task, we propose a modification of the Transformer architecture to take into account the entities predicted in the previous steps. We provide a set of experiments to study the model's capabilities and the effects of the order on performance. Finally, we show that the smallest to largest order gives the best results.

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“…The forest is constructed sequentially, where in each step the system decides which of the possible transition actions to execute, given its current state. Transition based parsing is also utilized by Marinho et al [23], but with a different set of parser actions and predicted representation.…”

Section: Neural Methodsmentioning

confidence: 99%

“…More details can be found in the PolEval subsection. [11] 74.5 66.0 70.0 Finkel and Manning [12] 75.4 65.9 70.3 Lu and Roth [13] 74.2 66.7 70.3 Muis and Lu [14] 75.4 66.8 70.8 Wang and Lu [15] 76.2 67.5 71.6 Neural methods Xu et al [17] 71.2 64.3 67.6 Katiyar and Cardie [16] 79.8 68.2 73.6 Ju et al [24] 78.5 71.3 74.7 Wang et al [22] 78.0 70.2 73.9 Wang and Lu [15] 77.0 73.3 75.1 Sohrab and Miwa [18] 93.2 64.0 77.1 Marinho et al [23] 74.0 72.0 73.0 Lin et al [19] 75.8 73.9 74.8 Zheng et al [25] 75.9 73.6 74.7 Sun et al [27] 77.4 74.9 76.2 Shibuya and Hovy [28] 78 To make a fair comparison with other publications, we preprocessed the corpus following the guidance of Finkel and Manning [12]. Their procedure, which involved splitting the dataset and reducing the number of entity types, was reused by most of the studies included in our comparison.…”

Section: A Hyperparameter Selectionmentioning

confidence: 99%

A Bidirectional Iterative Algorithm for Nested Named Entity Recognition

Dadas

Protasiewicz

2020

IEEE Access

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Nested named entity recognition (NER) is a special case of structured prediction in which annotated sequences can be contained inside each other. It is a challenging and significant problem in natural language processing. In this paper, we propose a novel framework for nested named entity recognition tasks. Our approach is based on a deep learning model which can be called in an iterative way, expanding the set of predicted entity mentions with each subsequent iteration. The proposed framework combines two such models trained to identify named entities in different directions: from general to specific (outside-in), and from specific to general (inside-out). The predictions of both models are then aggregated by a selection policy. We propose and evaluate several selection policies which can be used with our algorithm. Our method does not impose any restrictions on the length of entity mentions, number of entity classes, depth, or structure of the predicted output. The framework has been validated experimentally on four well-known nested named entity recognition datasets: GENIA, NNE, PolEval, and GermEval. The datasets differ in terms of domain (biomedical, news, mixed), language (English, Polish, German), and the structure of nesting (simple, complex). Through extensive tests, we prove that the approach we have proposed outperforms existing methods for nested named entity recognition. INDEX TERMS information extraction, natural language processing, nested named entity recognition,

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Hierarchical Nested Named Entity Recognition

Cited by 11 publications

References 30 publications

The Chilean Waiting List Corpus: a new resource for clinical Named Entity Recognition in Spanish

The Chilean Waiting List Corpus: a new resource for clinical Named Entity Recognition in Spanish

Effect of depth order on iterative nested named entity recognition models

A Bidirectional Iterative Algorithm for Nested Named Entity Recognition

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