• NER_Knowledge
• • Environment
  • MRC for NER
  • Single One-pass Model (SOne) for NER
  • Notice
  • • data_mrc
    • data_sone
    • RUN
    • decode

NER_Knowledge

Environment

python:3.5
tensorflow: 1.11

MRC for NER

use machine reading comprehension (MRC) model to solve NER task.
each data is a tuple (question,passage,start_pisition,end_position)
In NER, question is the lable definition for each entity type, passage is each sentence, start_position is the start position of each entity
and end_position is the end position of each entity.

Single One-pass Model (SOne) for NER

use single one-pass model to solve NER task.
Each data ia a tuple (passage, start_position1, end_position1, start_position2, end_position2, ...)

Notice

Because we use the last checkpoint of BERT to predict, so the development set is just to verify the performance of model.

data_mrc

we just set an example for mrc data

data_sone

we just set an example for SOne data
for SOne model, the type information is defined in advance. For example, normalize_bert.npy is bert representation of guideline information.

RUN

1. run MRC-based model

• bash run_mrc.sh

2. run SOne-based model

• bash run_sone.sh

decode

get the answer of submit file format
python trans2answer.py