18 Commits

Author	SHA1	Message	Date
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