424 Commits

Author	SHA1	Message	Date
svlandeg	e1213eaf6a	use original gold object in get_loss function	2019-07-18 13:35:10 +02:00
svlandeg	ec55d2fccd	filter training data beforehand (+black formatting)	2019-07-18 10:22:24 +02:00
Ines Montani	f2ea3e3ea2	Merge branch 'master' into feature/nel-wiki	2019-07-09 21:57:47 +02:00
Patrick Hogan	8c0586fd9c	Update example and sign contributor agreement (#3916) ... * Sign contributor agreement for askhogan * Remove unneeded `seen_tokens` which is never used within the scope	2019-07-08 10:27:20 +02:00
svlandeg	b7a0c9bf60	fixing the context/prior weight settings	2019-07-03 17:48:09 +02:00
svlandeg	8840d4b1b3	fix for context encoder optimizer	2019-07-03 13:35:36 +02:00
svlandeg	3420cbe496	small fixes	2019-07-03 10:25:51 +02:00
svlandeg	2d2dea9924	experiment with adding NER types to the feature vector	2019-06-29 14:52:36 +02:00
svlandeg	c664f58246	adding prior probability as feature in the model	2019-06-28 16:22:58 +02:00
svlandeg	1c80b85241	fix tests	2019-06-28 08:59:23 +02:00
svlandeg	68a0662019	context encoder with Tok2Vec + linking model instead of cosine	2019-06-28 08:29:31 +02:00
svlandeg	dbc53b9870	rename to KBEntryC	2019-06-26 15:55:26 +02:00
svlandeg	1de61f68d6	improve speed of prediction loop	2019-06-26 13:53:10 +02:00
svlandeg	bee23cd8af	try Tok2Vec instead of SpacyVectors	2019-06-25 16:09:22 +02:00
svlandeg	b58bace84b	small fixes	2019-06-24 10:55:04 +02:00
svlandeg	a31648d28b	further code cleanup	2019-06-19 09:15:43 +02:00
svlandeg	478305cd3f	small tweaks and documentation	2019-06-18 18:38:09 +02:00
svlandeg	0d177c1146	clean up code, remove old code, move to bin	2019-06-18 13:20:40 +02:00
svlandeg	ffae7d3555	sentence encoder only (removing article/mention encoder)	2019-06-18 00:05:47 +02:00
svlandeg	6332af40de	baseline performances: oracle KB, random and prior prob	2019-06-17 14:39:40 +02:00
svlandeg	24db1392b9	reprocessing all of wikipedia for training data	2019-06-16 21:14:45 +02:00
svlandeg	81731907ba	performance per entity type	2019-06-14 19:55:46 +02:00
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	d83a1e3052	Merge branch 'master' into feature/nel-wiki	2019-06-03 09:35:10 +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	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
Ines Montani	dd153b2b33	Simplify helper (see #3681) [ci skip]	2019-05-06 15:13:10 +02:00
Ines Montani	f8fce6c03c	Fix typo (see #3681)	2019-05-06 15:02:11 +02:00
Ines Montani	f2a56c1b56	Rewrite example to use Retokenizer (resolves #3681) ... Also add helper to filter spans	2019-05-06 14:51:18 +02:00
svlandeg	6961215578	refactor code to separate functionality into different files	2019-05-06 10:56:56 +02:00
svlandeg	f5190267e7	run only 100M of WP data as training dataset (9%)	2019-05-03 18:09:09 +02:00
svlandeg	4e929600e5	fix WP id parsing, speed up processing and remove ambiguous strings in one doc (for now)	2019-05-03 17:37:47 +02:00
svlandeg	34600c92bd	try catch per article to ensure the pipeline goes on	2019-05-03 15:10:09 +02:00
svlandeg	bbcb9da466	creating training data with clean WP texts and QID entities true/false	2019-05-03 10:44:29 +02:00
svlandeg	cba9680d13	run NER on clean WP text and link to gold-standard entity IDs	2019-05-02 17:24:52 +02:00
svlandeg	581dc9742d	parsing clean text from WP articles to use as input data for NER and NEL	2019-05-02 17:09:56 +02:00
svlandeg	8353552191	cleanup	2019-05-01 23:26:16 +02:00
svlandeg	1ae41daaa9	allow small rounding errors	2019-05-01 23:05:40 +02:00
svlandeg	3629a52ede	reading all persons in wikidata	2019-05-01 01:00:59 +02:00
svlandeg	60b54ae8ce	bulk entity writing and experiment with regex wikidata reader to speed up processing	2019-05-01 00:00:38 +02:00
svlandeg	653b7d9c87	calculate entity raw counts offline to speed up KB construction	2019-04-30 11:39:42 +02:00
svlandeg	19e8f339cb	deduce entity freq from WP corpus and serialize vocab in WP test	2019-04-29 17:37:29 +02:00
svlandeg	54d0cea062	unit test for KB serialization	2019-04-24 23:52:34 +02:00
svlandeg	3e0cb69065	KB aliases to and from file	2019-04-24 20:24:24 +02:00
svlandeg	ad6c5e581c	writing and reading number of entries to/from header	2019-04-24 15:31:44 +02:00
svlandeg	6e3223f234	bulk loading in proper order of entity indices	2019-04-24 11:26:38 +02:00
svlandeg	694fea597a	dumping all entryC entries + (inefficient) reading back in	2019-04-23 18:36:50 +02:00
svlandeg	8e70a564f1	custom reader and writer for _EntryC fields (first stab at it - not complete)	2019-04-23 16:33:40 +02:00
svlandeg	004e5e7d1c	little fixes	2019-04-19 14:24:02 +02:00
svlandeg	9a8197185b	fix alias capitalization	2019-04-18 22:37:50 +02:00
svlandeg	9f308eb5dc	fixes for prior prob and linking wikidata IDs with wikipedia titles	2019-04-18 16:14:25 +02:00
svlandeg	10ee8dfea2	poc with few entities and collecting aliases from the WP links	2019-04-18 14:12:17 +02:00
svlandeg	6763e025e1	parse wp dump for links to determine prior probabilities	2019-04-15 11:41:57 +02:00
svlandeg	3163331b1e	wikipedia dump parser and mediawiki format regex cleanup	2019-04-14 21:52:01 +02:00
svlandeg	b31a390a9a	reading types, claims and sitelinks	2019-04-11 21:42:44 +02:00
svlandeg	6e997be4b4	reading wikidata descriptions and aliases	2019-04-11 21:08:22 +02:00