Research Article
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 |
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