Pattern recognition (psychology)

Mathematics

ENCODE

Biology

Nested set model

Gene

Data mining

Statistics

Odds ratio

Nested loop join

Parallel computing

Artificial intelligence

Management

Economics

Nested case-control study

Chemistry

Natural language processing

Geometry

Sequence (biology)

Named-entity recognition

Computer science

Task (project management)

Relational database

Artificial neural network

Genetics

Biochemistry

Convolutional neural network

Word (group theory)

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 state-of-the-art results for CoNLL-2002 Dutch and Spanish and for CoNLL-2003 English.

Neural Architectures for Nested NER through Linearization