A Unified Model Using Distantly Supervised Data and Cross-Domain Data in NER

Algorithm 1

Overall training procedure.

(i)	Input: Hand data, cross-domain data, and distantly supervised data
(ii)	Output: Trained PARE model
(1)	for each epoch do
(2)	Merge hand data and cross-domain data
(3)	Divide merged data into many small bag1s
(4)	for each bag1 in bag1s do
(5)	for each sentence in bag1 do
(6)	Obtain the sentence state
(7)	if sentence in Hand data then
(8)
(9)	else
(10)	Select cross-domain data through
(11)	end if
(12)	end for
(13)	Obtain reward
(14)	Optimize CD data selector through (10)
(15)	end for
(16)	Merge hand data and distantly supervised data
(17)	Divide merged data into many small bag2s
(18)	for each bag2 in bag2s do
(19)	for each sentence in bag2 do
(20)	Obtain the sentence state
(21)	if sentence in Hand data then
(22)
(23)	else
(24)	Select distantly supervised data through
(25)	end if
(26)	end for
(27)	Obtain reward
(28)	Optimize DS data selector through (10)
(29)	end for
(30)	Train the core NER using selected data
(31)	end for