Nested-NER

Nested-NER is an implementation of [A Neural Layered Model for Nested Named Entity Recognition] (http://aclweb.org/anthology/N18-1131).

Requirements

• Ubuntu 16.04
• chainer 3.3.0
• python 3.5.2
• numpy 1.14.1
• cupy 2.4.0
• cuda 9.1
• cudnn 7.0

Data format

Each sentence is separated by an empty line. Each line is separated by tab key. Each line contains

word	label1	label2	label3	...	labelN

This sentence, John killed Mary's husband. , it contains three entities: John (PER), Mary(PER), Mary's husband(PER). The example format is listed as following:

John    B-PER   O   O
killed  O   O   O
Mary    B-PER   B-PER   O
's  O   I-PER   O
husband O   I-PER   O
.   O   O   O

Pretrained word embeddings

• Pretrained word embeddings used in GENIA: https://drive.google.com/open?id=0BzMCqpcgEJgiUWs0ZnU0NlFTam8
• Pretrained word embeddings used in ACE2005:http://tti-coin.jp/data/ace2005-test.txt.gz

Configuration

Parameters are listed in the config file which is located in the nested-ner/src folder. Before running the codes, please change the parameters with specific requirements.

Usage

Training

cd nested-ner/src/
python3 train.py

Testing

cd nested-ner/src
python3 test.py

Please cite our NAACL paper when using this code.

• Meizhi Ju, Makoto Miwa, Sophia Ananiadou. A Neural Layered Model for Nested Named Entity Recognition In the Proceedings of NAACL-HLT2018, pp. 1446--1459.