A Neural Layered Model for Nested Named Entity Recognition

Abstract

Entity mentions embedded in longer entity mentions are referred to as nested entities. Most named entity recognition (NER) systems deal only with the flat entities and ignore the inner nested ones, which fails to capture finer-grained semantic information in underlying texts. To address this issue, we propose a novel neural model to identify nested entities by dynamically stacking flat NER layers. Each flat NER layer is based on the state-ofthe-art flat NER model that captures sequential context representation with bidirectional long short-term memory (LSTM) layer and feeds it to the cascaded CRF layer. Our model merges the output of the LSTM layer in the current flat NER layer to build new representation for detected entities and subsequently feeds them into the next flat NER layer. This allows our model to extract outer entities by taking full advantage of information encoded in their corresponding inner entities, in an inside-to-outside way. Our model dynamically stacks the flat NER layers until no outer entities are extracted. Extensive evaluation shows that our dynamic model outperforms state-ofthe-art feature-based systems on nested NER, achieving 74.7% and 72.2% on GENIA and ACE2005 datasets, respectively, in terms of Fscore.

A Search-based Neural Model for Biomedical Nested and Overlapping Event Detection

“…These results set the first focussed benchmark of our model and next steps include applying it to other event datasets in the biomedical and general domain. In addition, it can also be applied to other DAG structures such as nested/discontiguous entities (Muis and Lu, 2016;Ju et al, 2018).…”

Multi-grained Named Entity Recognition

“…Finkel and Manning (2009) propose a CRF-based constituency parser which takes each named entity as a constituent in the parsing tree. Ju et al (2018) dynamically stack multiple flat NER layers and extract outer entities based on the inner ones. Such model may suffer from the error propagation problem if shorter entities are recognized incorrectly.…”

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

“…For the ACE corpus, the default metric in the literature Ju et al, 2018; does not include sequential ordering of nested entities (as many architectures do not have a concept of ordered nested outputs). As a result, an entity is considered correct if it is present in the target labels, regardless of which layer the model predicts it on.…”

“…Multigraph + MS (Muis and Lu, 2017) 69.1 58.1 63.1 RNN + hyp (Katiyar and Cardie, 2018) 70.6 70.4 70.5 BiLSTM-CRF stacked (Ju et al, 2018) 74.2 70.3 72.2 LSTM + forest [POS] Given the recent success on many tasks using contextual word embeddings, we also evaluate performance using the output of pre-trained BERT (Devlin et al, 2018) and ELMO (Peters et al, 2018) models as input embeddings. This leads to a significant jump in performance to 78.9 with ELMO, and 82.4 with BERT (both avg.…”