Commit Graph

20 Commits

Author SHA1 Message Date
svlandeg
7b13e3d56f undersampling negatives 2019-05-21 18:35:10 +02:00
svlandeg
2fa3fac851 fix concat bp and more efficient batch calls 2019-05-21 13:43:59 +02:00
svlandeg
0a15ee4541 fix in bp call 2019-05-20 23:54:55 +02:00
svlandeg
89e322a637 small fixes 2019-05-20 17:20:39 +02:00
svlandeg
7edb2e1711 fix convolution layer 2019-05-20 11:58:48 +02:00
svlandeg
dd691d0053 debugging 2019-05-17 17:44:11 +02:00
svlandeg
400b19353d simplify architecture and larger-scale test runs 2019-05-17 01:51:18 +02:00
svlandeg
d51bffe63b clean up code 2019-05-16 18:36:15 +02:00
svlandeg
b5470f3d75 various tests, architectures and experiments 2019-05-16 18:25:34 +02:00
svlandeg
9ffe5437ae calculate gradient for entity encoding 2019-05-15 02:23:08 +02:00
svlandeg
2713abc651 implement loss function using dot product and prob estimate per candidate cluster 2019-05-14 22:55:56 +02:00
svlandeg
09ed446b20 different architecture / settings 2019-05-14 08:37:52 +02:00
svlandeg
4142e8dd1b train and predict per article (saving time for doc encoding) 2019-05-13 17:02:34 +02:00
svlandeg
3b81b00954 evaluating on dev set during training 2019-05-13 14:26:04 +02:00
svlandeg
b6d788064a some first experiments with different architectures and metrics 2019-05-10 12:53:14 +02:00
svlandeg
9d089c0410 grouping clusters of instances per doc+mention 2019-05-09 18:11:49 +02:00
svlandeg
c6ca8649d7 first stab at model - not functional yet 2019-05-09 17:23:19 +02:00
svlandeg
9f33732b96 using entity descriptions and article texts as input embedding vectors for training 2019-05-07 16:03:42 +02:00
svlandeg
7e348d7f7f baseline evaluation using highest-freq candidate 2019-05-06 15:13:50 +02:00
svlandeg
6961215578 refactor code to separate functionality into different files 2019-05-06 10:56:56 +02:00