TraConcept: Constructing a Concept Framework from Chinese Traffic Legal Texts

Requirements

conda env: /penngao_conda_environment.yaml

pip package: /penngao_pip_packages.txt

Datasets

HITT-h: /examples/training/hypernymy/datasets/hypernymydetection.tsv.gz

HITT-a: /examples/training/attribute/datasets/attributedetection.tsv.gz

HITT-m: /examples/training/multirelation/datasets/multi-relation-detection-detection.tsv.gz

Statistical information of datasets is below:

Datasets	Train	Dev	Test
HITT-h	18,847	6,302	6,292
HITT-a	40,412	13,449	13,451
HITT-m	19,100	6,120	6,515

Main File

Hypernymy Detection: /examples/training/hypernymy/training_hypernymy_benchmark.py

Concept Attribute Detection: /examples/training/attribute/training_attribute_benchmark.py

Multi-relation Detection: /examples/training/multirelation/training_multi_relation_benchmark.py

Run

python training_*_benchmark.py

Results

Binary relation detection:

Model	Dataset	Accuracy	Precision	Recall	F1
D-Tensor	HITT-h	87.78	74.88	61.56	67.45
D-Tensor	HITT-a	83.27	70.15	60.18	65.38
Bran	HITT-h	91.52	82.31	79.68	81.32
Bran	HITT-a	85.34	71.25	65.48	68.56
U_Teal	HITT-h	78.47	41.55	9.38	15.30
U_Teal	HITT-a	77.36	40.15	10.16	16.20
S_Teal	HITT-h	90.85	87.03	84.31	85.56
S_Teal	HITT-a	89.90	74.22	73.44	73.83
AS_Teal	HITT-h	93.36	88.67	86.22	87.89
AS_Teal	HITT-a	92.92	80.89	79.60	79.70
CEE	HITT-h	92.34	85.25	83.56	84.46
CEE	HITT-a	88.56	72.17	70.86	71.56
Ours	HITT-h	93.00	87.88	89.79	88.18
Ours	HITT-a	91.09	77.66	81.02	79.31

D-Tensor: Dual tensor model for detecting asymmetric lexicosemantic relations. EMNLP 2017

Bran: Simultaneously self-attending to all mentions for full-abstract biological relation extraction. NAACL 2018

Teal: Improving hypernymy prediction via taxonomy enhanced adversarial learning. AAAI 2019

CCE: Learning Conceptual-Contextual Embeddings for Medical Text. AAAI 2020

Multi-relation detection:

Model	Dataset	Accuracy	Macro_p	Macro_R	Macro_F1
D-Tensor	HITT-m	75.30	76.52	73.34	74.78
Bran	HITT-m	78.89	79.32	75.18	77.56
CCE	HITT-m	80.12	65.89	75.30	69.53
Ours	HITT-m	81.57	82.23	81.56	81.80

Reference

[SentenceTransformers](SentenceTransformers Documentation — Sentence-Transformers documentation (sbert.net))

refer to Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks(EMNLP 2019)