Output format: training-output to build supervised models
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# Conflicts: # bioresources/src/main/resources/org/clulab/reach/kb/NER-Grounding-Override.tsv.gz # bioresources/src/main/resources/org/clulab/reach/kb/ner/BioProcess.tsv.gz # bioresources/src/main/resources/org/clulab/reach/kb/ner/CellLine.tsv.gz # bioresources/src/main/resources/org/clulab/reach/kb/ner/CellType.tsv.gz # bioresources/src/main/resources/org/clulab/reach/kb/ner/Cellular_component.tsv.gz # bioresources/src/main/resources/org/clulab/reach/kb/ner/Disease.tsv.gz # bioresources/src/main/resources/org/clulab/reach/kb/ner/Family.tsv.gz # bioresources/src/main/resources/org/clulab/reach/kb/ner/Gene_or_gene_product.tsv.gz # bioresources/src/main/resources/org/clulab/reach/kb/ner/Organ.tsv.gz # bioresources/src/main/resources/org/clulab/reach/kb/ner/Simple_chemical.tsv.gz # bioresources/src/main/resources/org/clulab/reach/kb/ner/Site.tsv.gz # bioresources/src/main/resources/org/clulab/reach/kb/ner/Species.tsv.gz # bioresources/src/main/resources/org/clulab/reach/kb/ner/TissueType.tsv.gz
…statistics events. Added support for significance and confidence intervals to the arizona output (and possibly to the CMU by extension)
# Conflicts: # src/main/scala/org/clulab/reach/ReachCLI.scala
kwalcock
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Sep 12, 2023
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The crossScalaVersion does need to be changed for publication.
export/src/main/scala/org/clulab/reach/export/TrainingDataExporter.scala
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@enoriega, this is being built for both Scala 2.11 and 2.12. The earlier version does not like trailing/dangling commas like the ones in TrainingDataExporter, so it doesn't compile. One can use |
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That TrainingDataExporter still needs a comma removed at line 76 in order to work on Scala 2.11. |
kwalcock
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Sep 22, 2023
…se its published version
# Conflicts: # main/src/main/resources/application.conf
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Summary
Added a new output format suitable to train classifiers using a python pipeline. It "flattens" activations and regulations and creates a json array with the tokens, spans, label and polarity for each event.
Example
[ { "sentence_tokens" : [ "Notably", ",", "overexpressing", "MafB", "in", "human", "beta-cell", "lines", "(", "beta", "TC3", "cells", ")", "resulted", "in", "increased", "cell", "proliferation", "by", "upregulating", "important", "cell", "cycle", "regulators", ",", "like", "cyclin", "D2", "and", "cyclin", "B", "(", "28", ")", "." ], "event_indices" : [ 16, 17, 18, 19, 20, 21, 22, 23 ], "type" : "Positive_activation", "polarity" : true, "controller_indices" : [ 16, 17, 18 ], "controlled_indices" : [ 21, 22, 23 ], "trigger_indices" : [ 19, 20 ] }, { "sentence_tokens" : [ "In", "vivo", "glucose", "stimulated", "insulin", "secretion", "(", "GSIS", ")", "experiment", ".." ], "event_indices" : [ 2, 3, 4, 5, 6 ], "type" : "Positive_activation", "polarity" : true, "controller_indices" : [ 2, 3 ], "controlled_indices" : [ 4, 5, 6 ], "trigger_indices" : [ 3, 4 ] } ]