A large-scale Chinese patent dataset for information extraction

Figures & data

Figure 1. The correspondence between entity and relation.

Table 1. Entity types and relation types.

Entity types	Relation types
Subject entity:	Spatial relation
Component
Method	partial relation
Material
Value	Method advantage
Object entity:
Location	material advantage
Part
Effect	parameter relation
Attribution

Table 2. Comparison of annotation tools.

Tool's Name	YEDDA	Doccano	Brat	EasyData
Platform	Windows	Windows	Linux	Web
Types of tasks	NER	NER	NER	NER
		Emotion classification	IE	RE
		Machine translation		Text classification
				Emotion classification
Installation Required	Yes	Yes	Yes	No

Figure 2. Statistics of entity labels.

Figure 3. Statistics of relation labels.

Figure 4. Example of a sample (Named entity identification).

Figure 5. Example of a sample (Relationship classification).

Figure 6. Flowchart of the patent information extraction.

Figure 7. The framework of the BiLSTM based NER method.

Table 3. NER results of Bi-LSTM models.

	chinese-bert-base				chinese-roberta-wwm-ext
	Precision	Recall	F1-score	Support	Precision	Recall	F1-score	Support
Object	0.41	0.28	0.33	460	0.43	0.29	0.34	460
Subject	0.47	0.31	0.37	569	0.48	0.31	0.38	569
Macro avg	0.44	0.29	0.35	1029	0.46	0.30	0.36	1029

Table 4. RE experimental results.

	Precision	Recall	F1-score	Support
Spatial relation	0.95	0.90	0.93	494
Partial relation	0.96	0.98	0.97	1513
Method advantage	0.77	0.90	0.83	201
Material advantage	0.91	0.63	0.74	150
Parameter relation	0.98	1.00	0.99	89
Macro avg	0.91	0.88	0.89	2447

Table 5. SPO experimental results.

Precision	Recall	F1-score
0.068	0.071	0.07

Data availability statement

The data can be available from the corresponding author upon reasonable request.