42 Commits

Author	SHA1	Message	Date
svlandeg	b312f2d0e7	redo training data to be independent of KB and entity-level instead of doc-level	2019-06-14 15:55:26 +02:00
svlandeg	0b04d142de	regenerating KB	2019-06-13 22:32:56 +02:00
svlandeg	78dd3e11da	write entity linking pipe to file and keep vocab consistent between kb and nlp	2019-06-13 16:25:39 +02:00
svlandeg	b12001f368	small fixes	2019-06-12 22:05:53 +02:00
svlandeg	6521cfa132	speeding up training	2019-06-12 13:37:05 +02:00
svlandeg	66813a1fdc	speed up predictions	2019-06-11 14:18:20 +02:00
svlandeg	fe1ed432ef	eval on dev set, varying combo's of prior and context scores	2019-06-11 11:40:58 +02:00
svlandeg	83dc7b46fd	first tests with EL pipe	2019-06-10 21:25:26 +02:00
svlandeg	7de1ee69b8	training loop in proper pipe format	2019-06-07 15:55:10 +02:00
svlandeg	0486ccabfd	introduce goldparse.links	2019-06-07 13:54:45 +02:00
svlandeg	a5c061f506	storing NEL training data in GoldParse objects	2019-06-07 12:58:42 +02:00
svlandeg	61f0e2af65	code cleanup	2019-06-06 20:22:14 +02:00
svlandeg	d8b435ceff	pretraining description vectors and storing them in the KB	2019-06-06 19:51:27 +02:00
svlandeg	5c723c32c3	entity vectors in the KB + serialization of them	2019-06-05 18:29:18 +02:00
svlandeg	9abbd0899f	separate entity encoder to get 64D descriptions	2019-06-05 00:09:46 +02:00
svlandeg	fb37cdb2d3	implementing el pipe in pipes.pyx (not tested yet)	2019-06-03 21:32:54 +02:00
svlandeg	9e88763dab	60% acc run	2019-06-03 08:04:49 +02:00
svlandeg	268a52ead7	experimenting with cosine sim for negative examples (not OK yet)	2019-05-29 16:07:53 +02:00
svlandeg	a761929fa5	context encoder combining sentence and article	2019-05-28 18:14:49 +02:00
svlandeg	992fa92b66	refactor again to clusters of entities and cosine similarity	2019-05-28 00:05:22 +02:00
svlandeg	8c4aa076bc	small fixes	2019-05-27 14:29:38 +02:00
svlandeg	cfc27d7ff9	using Tok2Vec instead	2019-05-26 23:39:46 +02:00
svlandeg	abf9af81c9	learn rate en epochs	2019-05-24 22:04:25 +02:00
svlandeg	86ed771e0b	adding local sentence encoder	2019-05-23 16:59:11 +02:00
svlandeg	4392c01b7b	obtain sentence for each mention	2019-05-23 15:37:05 +02:00
svlandeg	97241a3ed7	upsampling and batch processing	2019-05-22 23:40:10 +02:00
svlandeg	1a16490d20	update per entity	2019-05-22 12:46:40 +02:00
svlandeg	eb08bdb11f	hidden with for encoders	2019-05-21 23:42:46 +02:00
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	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	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	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