svlandeg
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5c723c32c3
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entity vectors in the KB + serialization of them
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2019-06-05 18:29:18 +02:00 |
svlandeg
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9abbd0899f
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separate entity encoder to get 64D descriptions
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2019-06-05 00:09:46 +02:00 |
svlandeg
|
fb37cdb2d3
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implementing el pipe in pipes.pyx (not tested yet)
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2019-06-03 21:32:54 +02:00 |
svlandeg
|
9e88763dab
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60% acc run
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2019-06-03 08:04:49 +02:00 |
svlandeg
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268a52ead7
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experimenting with cosine sim for negative examples (not OK yet)
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2019-05-29 16:07:53 +02:00 |
svlandeg
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a761929fa5
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context encoder combining sentence and article
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2019-05-28 18:14:49 +02:00 |
svlandeg
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992fa92b66
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refactor again to clusters of entities and cosine similarity
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2019-05-28 00:05:22 +02:00 |
svlandeg
|
8c4aa076bc
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small fixes
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2019-05-27 14:29:38 +02:00 |
svlandeg
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cfc27d7ff9
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using Tok2Vec instead
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2019-05-26 23:39:46 +02:00 |
svlandeg
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abf9af81c9
|
learn rate en epochs
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2019-05-24 22:04:25 +02:00 |
svlandeg
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86ed771e0b
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adding local sentence encoder
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2019-05-23 16:59:11 +02:00 |
svlandeg
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4392c01b7b
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obtain sentence for each mention
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2019-05-23 15:37:05 +02:00 |
svlandeg
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97241a3ed7
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upsampling and batch processing
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2019-05-22 23:40:10 +02:00 |
svlandeg
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1a16490d20
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update per entity
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2019-05-22 12:46:40 +02:00 |
svlandeg
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eb08bdb11f
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hidden with for encoders
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2019-05-21 23:42:46 +02:00 |
svlandeg
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7b13e3d56f
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undersampling negatives
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2019-05-21 18:35:10 +02:00 |
svlandeg
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2fa3fac851
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fix concat bp and more efficient batch calls
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2019-05-21 13:43:59 +02:00 |
svlandeg
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0a15ee4541
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fix in bp call
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2019-05-20 23:54:55 +02:00 |
svlandeg
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dd691d0053
|
debugging
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2019-05-17 17:44:11 +02:00 |
svlandeg
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400b19353d
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simplify architecture and larger-scale test runs
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2019-05-17 01:51:18 +02:00 |
svlandeg
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b5470f3d75
|
various tests, architectures and experiments
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2019-05-16 18:25:34 +02:00 |
svlandeg
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9ffe5437ae
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calculate gradient for entity encoding
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2019-05-15 02:23:08 +02:00 |
svlandeg
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2713abc651
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implement loss function using dot product and prob estimate per candidate cluster
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2019-05-14 22:55:56 +02:00 |
svlandeg
|
09ed446b20
|
different architecture / settings
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2019-05-14 08:37:52 +02:00 |
svlandeg
|
4142e8dd1b
|
train and predict per article (saving time for doc encoding)
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2019-05-13 17:02:34 +02:00 |
svlandeg
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c6ca8649d7
|
first stab at model - not functional yet
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2019-05-09 17:23:19 +02:00 |
svlandeg
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9f33732b96
|
using entity descriptions and article texts as input embedding vectors for training
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2019-05-07 16:03:42 +02:00 |
svlandeg
|
7e348d7f7f
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baseline evaluation using highest-freq candidate
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2019-05-06 15:13:50 +02:00 |
svlandeg
|
6961215578
|
refactor code to separate functionality into different files
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2019-05-06 10:56:56 +02:00 |