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 |
|
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
|
387263d618
|
simplify chains
|
2019-04-29 13:58:07 +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 |
|