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

Algorithm 2

The training procedure in no in-domain hand-annotated data.

(i)	Input: Cross-domain data and distantly supervised data
(ii)	Output: Trained PARE model
(1)	Merge distantly supervised data and cross-domain data.
(2)	for Each epoch do
(3)	Divide the merge 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 distantly supervised 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 the selected cross-domain sentences and distantly supervised data
(17)	Divide the 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 cross-domain 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