Enhancing Joint Entity and Relation Extraction with Language Modeling and Hierarchical Attention

Implementation of the papers [Enhancing Joint Entity and Relation Extraction with Language Modeling and Hierarchical Attention].

Requirements

Ubuntu 16.04
Anaconda 5.0.1
Numpy 1.14.1
Gensim 3.4.0
Tensorflow 1.5.0
PrettyTable 0.7.2

Task

Given a sequence of tokens (i.e., sentence), (i) give the entity tag of each word (e.g., NER) and (ii) the relations between the entities in the sentence. The following example indicates the accepted input format of our multi-head selection model:

0	Marc		B-PER		['N']					[0]		
1	Smith		I-PER 		['lives_in','works_for']		[5,11]
2 	lives		O		['N']					[2]
3	in		O		['N']					[3]
4	New		B-LOC		['N']					[4]
5	Orleans		I-LOC		['N']					[5] 
6	and		O		['N']					[6]
7	is		O		['N']					[7]
8	hired		O		['N']					[8]
9	by		O		['N']					[9]
10	the		O		['N']					[10]
11  government		B-ORG		['N']					[11]
12	.		O		['N']					[12]

Configuration

The model has several parameters such as:

EC (entity classification) or BIO (BIO encoding scheme)
Character embeddings
Ner loss (softmax or CRF)

Run the model

./run.sh

More details

Commands executed in ./run.sh:

Train on the training set and evaluate on the dev set to obtain early stopping epoch python3 train_es.py
Train on the concatenated (train + dev) set and evaluate on the test set until either (1) the max epochs or (2) the early stopping limit (specified by train_es.py) is exceeded python3 train_eval.py

ChiRenjun / Joint-extraction-with-lm-and-att