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								 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 |  | 
			
				
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								 svlandeg | 0b04d142de | regenerating KB | 2019-06-13 22:32:56 +02:00 |  | 
			
				
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								 svlandeg | 78dd3e11da | write entity linking pipe to file and keep vocab consistent between kb and nlp | 2019-06-13 16:25:39 +02:00 |  | 
			
				
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								 svlandeg | b12001f368 | small fixes | 2019-06-12 22:05:53 +02:00 |  | 
			
				
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								 svlandeg | 6521cfa132 | speeding up training | 2019-06-12 13:37:05 +02:00 |  | 
			
				
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								 svlandeg | 66813a1fdc | speed up predictions | 2019-06-11 14:18:20 +02:00 |  | 
			
				
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								 svlandeg | fe1ed432ef | eval on dev set, varying combo's of prior and context scores | 2019-06-11 11:40:58 +02:00 |  | 
			
				
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								 svlandeg | 83dc7b46fd | first tests with EL pipe | 2019-06-10 21:25:26 +02:00 |  | 
			
				
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								 svlandeg | 7de1ee69b8 | training loop in proper pipe format | 2019-06-07 15:55:10 +02:00 |  | 
			
				
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								 svlandeg | 0486ccabfd | introduce goldparse.links | 2019-06-07 13:54:45 +02:00 |  | 
			
				
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								 svlandeg | a5c061f506 | storing NEL training data in GoldParse objects | 2019-06-07 12:58:42 +02:00 |  | 
			
				
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								 svlandeg | 61f0e2af65 | code cleanup | 2019-06-06 20:22:14 +02:00 |  | 
			
				
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								 svlandeg | d8b435ceff | pretraining description vectors and storing them in the KB | 2019-06-06 19:51:27 +02:00 |  | 
			
				
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								 svlandeg | 5c723c32c3 | entity vectors in the KB + serialization of them | 2019-06-05 18:29:18 +02:00 |  | 
			
				
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								 svlandeg | 9abbd0899f | separate entity encoder to get 64D descriptions | 2019-06-05 00:09:46 +02:00 |  | 
			
				
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								 svlandeg | fb37cdb2d3 | implementing el pipe in pipes.pyx (not tested yet) | 2019-06-03 21:32:54 +02:00 |  | 
			
				
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								 svlandeg | 9e88763dab | 60% acc run | 2019-06-03 08:04:49 +02:00 |  | 
			
				
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								 svlandeg | 268a52ead7 | experimenting with cosine sim for negative examples (not OK yet) | 2019-05-29 16:07:53 +02:00 |  | 
			
				
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								 svlandeg | a761929fa5 | context encoder combining sentence and article | 2019-05-28 18:14:49 +02:00 |  | 
			
				
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								 svlandeg | 992fa92b66 | refactor again to clusters of entities and cosine similarity | 2019-05-28 00:05:22 +02:00 |  | 
			
				
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								 svlandeg | 8c4aa076bc | small fixes | 2019-05-27 14:29:38 +02:00 |  | 
			
				
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								 svlandeg | cfc27d7ff9 | using Tok2Vec instead | 2019-05-26 23:39:46 +02:00 |  | 
			
				
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								 svlandeg | abf9af81c9 | learn rate en epochs | 2019-05-24 22:04:25 +02:00 |  | 
			
				
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								 svlandeg | 86ed771e0b | adding local sentence encoder | 2019-05-23 16:59:11 +02:00 |  | 
			
				
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								 svlandeg | 4392c01b7b | obtain sentence for each mention | 2019-05-23 15:37:05 +02:00 |  | 
			
				
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								 svlandeg | 97241a3ed7 | upsampling and batch processing | 2019-05-22 23:40:10 +02:00 |  | 
			
				
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								 svlandeg | 1a16490d20 | update per entity | 2019-05-22 12:46:40 +02:00 |  | 
			
				
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								 svlandeg | eb08bdb11f | hidden with for encoders | 2019-05-21 23:42:46 +02:00 |  | 
			
				
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								 svlandeg | 7b13e3d56f | undersampling negatives | 2019-05-21 18:35:10 +02:00 |  | 
			
				
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								 svlandeg | 2fa3fac851 | fix concat bp and more efficient batch calls | 2019-05-21 13:43:59 +02:00 |  | 
			
				
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								 svlandeg | 0a15ee4541 | fix in bp call | 2019-05-20 23:54:55 +02:00 |  | 
			
				
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								 svlandeg | 89e322a637 | small fixes | 2019-05-20 17:20:39 +02:00 |  | 
			
				
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								 svlandeg | 7edb2e1711 | fix convolution layer | 2019-05-20 11:58:48 +02:00 |  | 
			
				
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								 svlandeg | dd691d0053 | debugging | 2019-05-17 17:44:11 +02:00 |  | 
			
				
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								 svlandeg | 400b19353d | simplify architecture and larger-scale test runs | 2019-05-17 01:51:18 +02:00 |  | 
			
				
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								 svlandeg | d51bffe63b | clean up code | 2019-05-16 18:36:15 +02:00 |  | 
			
				
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								 svlandeg | b5470f3d75 | various tests, architectures and experiments | 2019-05-16 18:25:34 +02:00 |  | 
			
				
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								 svlandeg | 9ffe5437ae | calculate gradient for entity encoding | 2019-05-15 02:23:08 +02:00 |  | 
			
				
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								 svlandeg | 2713abc651 | implement loss function using dot product and prob estimate per candidate cluster | 2019-05-14 22:55:56 +02:00 |  | 
			
				
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								 svlandeg | 09ed446b20 | different architecture / settings | 2019-05-14 08:37:52 +02:00 |  | 
			
				
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								 svlandeg | 4142e8dd1b | train and predict per article (saving time for doc encoding) | 2019-05-13 17:02:34 +02:00 |  | 
			
				
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								 svlandeg | 3b81b00954 | evaluating on dev set during training | 2019-05-13 14:26:04 +02:00 |  | 
			
				
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								 svlandeg | b6d788064a | some first experiments with different architectures and metrics | 2019-05-10 12:53:14 +02:00 |  | 
			
				
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								 svlandeg | 9d089c0410 | grouping clusters of instances per doc+mention | 2019-05-09 18:11:49 +02:00 |  | 
			
				
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								 svlandeg | c6ca8649d7 | first stab at model - not functional yet | 2019-05-09 17:23:19 +02:00 |  | 
			
				
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								 svlandeg | 9f33732b96 | using entity descriptions and article texts as input embedding vectors for training | 2019-05-07 16:03:42 +02:00 |  | 
			
				
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								 svlandeg | 7e348d7f7f | baseline evaluation using highest-freq candidate | 2019-05-06 15:13:50 +02:00 |  | 
			
				
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								 svlandeg | 6961215578 | refactor code to separate functionality into different files | 2019-05-06 10:56:56 +02:00 |  |