Improving Loanword Identification in Low-Resource Language with Data Augmentation and Multiple Feature Fusion

Table 5

Loanword identification experimental results on different methods.

Donor	Model	Loanword identification results (%)
				R		F1	F1 (+)
Russian	Rule (+)	72.04	72.89	69.31	70.18	70.65	71.28
	CRF (+)	71.63	72.45	67.28	68.15	69.39	70.23
	BLSTM-CNN (+)	71.45	72.26	70.50	71.31	70.97	71.78
	ClEmbedding (+)	73.12	73.94	71.84	72.62	72.47	73.27
	Ours (+)	74.80	75.62	73.64	74.20	74.22	74.90
Arabic	Rule (+)	69.05	69.84	68.17	69.02	68.61	69.43
	CRF (+)	69.83	70.65	67.42	68.29	68.60	69.45
	BLSTM-CNN (+)	68.70	69.52	69.85	70.67	69.27	70.09
	ClEmbedding (+)	72.95	73.76	72.03	72.85	72.49	73.30
	Ours (+)	73.91	74.62	72.35	73.06	73.12	73.83
Turkish	Rule (+)	72.02	72.86	69.87	70.50	70.93	71.66
	CRF (+)	71.46	72.29	69.02	69.95	70.22	71.10
	BLSTM-CNN (+)	71.25	72.04	70.43	71.18	70.84	71.61
	ClEmbedding (+)	72.96	73.64	73.08	73.85	73.02	73.74
	Ours (+)	75.24	76.09	74.36	75.14	74.80	75.61
Chinese	Rule (+)	70.32	71.13	69.77	70.58	70.04	70.85
	CRF (+)	70.85	71.64	69.24	70.05	70.04	70.84
	BLSTM-CNN (+)	70.58	71.34	69.98	70.79	70.28	71.06
	ClEmbedding (+)	71.67	72.48	71.35	72.14	71.51	72.31
	Ours (+)	74.30	75.07	72.88	73.95	73.58	74.51