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 line has multiple columns separated by a tab key.
Each line contains
word label1 label2 label3 ... labelN
The number of labels (N) for each word is determined by the maximum nested level in the data set. N=maximum nested level + 1
Each sentence is separated by an empty line.
For example, for these two sentences, John killed Mary's husband. He was arrested last night , they contain four entities: John (PER), Mary(PER), Mary's husband(PER),He (PER).
The format for these two sentences 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
He B-PER O O
was O O O
arrested O O O
last O O O
night O O 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/wikipedia200.bin
Configuration
Parameters are listed in the config file which is located in the layered-bilstm-crf/src folder.
Before running the codes, please change the parameters with specific values.
Usage
Training
cd layered-bilstm-crf/src/
python3 train.py
Testing
cd layered-bilstm-crf/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.