Description
Relation extraction between body parts entities like Internal_organ_or_component, External_body_part_or_region etc. and Direction entities like upper, lower in clinical texts.
Predicted Entities
1 : Shows the body part and direction entity are related.
0 : Shows the body part and direction entity are not related.
How to use
...
words_embedder = WordEmbeddingsModel() \
.pretrained("embeddings_clinical", "en", "clinical/models") \
.setInputCols(["sentences", "tokens"]) \
.setOutputCol("embeddings")
ner_tagger = NerDLModel() \
.pretrained("jsl_ner_wip_greedy_clinical", "en", "clinical/models") \
.setInputCols(["sentences", "tokens", "embeddings"]) \
.setOutputCol("ner_tags")
ner_converter = NerConverter() \
.setInputCols(["sentences", "tokens", "ner_tags"]) \
.setOutputCol("ner_chunks")
dependency_parser = DependencyParserModel() \
.pretrained("dependency_conllu", "en") \
.setInputCols(["sentences", "pos_tags", "tokens"]) \
.setOutputCol("dependencies")
# Set a filter on pairs of named entities which will be treated as relation candidates
re_ner_chunk_filter = RENerChunksFilter() \
.setInputCols(["ner_chunks", "dependencies"])\
.setMaxSyntacticDistance(10)\
.setOutputCol("re_ner_chunks").setRelationPairs(['direction-internal_organ_or_component', 'internal_organ_or_component-direction'])
# The dataset this model is trained to is sentence-wise.
# This model can also be trained on document-level relations - in which case, while predicting, use "document" instead of "sentence" as input.
re_model = RelationExtractionDLModel()\
.pretrained('redl_bodypart_direction_biobert', 'en', "clinical/models") \
.setPredictionThreshold(0.5)\
.setInputCols(["re_ner_chunks", "sentences"]) \
.setOutputCol("relations")
pipeline = Pipeline(stages=[documenter, sentencer, tokenizer, pos_tagger, words_embedder, ner_tagger, ner_converter, dependency_parser, re_ner_chunk_filter, re_model])
text ="MRI demonstrated infarction in the upper brain stem , left cerebellum and right basil ganglia"
p_model = pipeline.fit(spark.createDataFrame([[text]]).toDF("text"))
result = p_model.transform(data)
...
val words_embedder = WordEmbeddingsModel()
.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens"))
.setOutputCol("embeddings")
val ner_tagger = NerDLModel()
.pretrained("ner_clinical", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens", "embeddings"))
.setOutputCol("ner_tags")
val ner_converter = NerConverter()
.setInputCols(Array("sentences", "tokens", "ner_tags"))
.setOutputCol("ner_chunks")
val dependency_parser = DependencyParserModel()
.pretrained("dependency_conllu", "en")
.setInputCols(Array("sentences", "pos_tags", "tokens"))
.setOutputCol("dependencies")
// Set a filter on pairs of named entities which will be treated as relation candidates
val re_ner_chunk_filter = RENerChunksFilter()
.setInputCols(Array("ner_chunks", "dependencies"))
.setMaxSyntacticDistance(10)
.setOutputCol("re_ner_chunks").setRelationPairs(Array("direction-internal_organ_or_component", "internal_organ_or_component-direction"))
// The dataset this model is trained to is sentence-wise.
// This model can also be trained on document-level relations - in which case, while predicting, use "document" instead of "sentence" as input.
val re_model = RelationExtractionDLModel()
.pretrained("redl_bodypart_direction_biobert", "en", "clinical/models")
.setPredictionThreshold(0.5)
.setInputCols(Array("re_ner_chunks", "sentences"))
.setOutputCol("relations")
val pipeline = new Pipeline().setStages(Array(documenter, sentencer, tokenizer, pos_tagger, words_embedder, ner_tagger, ner_converter, dependency_parser, re_ner_chunk_filter, re_model))
val result = pipeline.fit(Seq.empty["MRI demonstrated infarction in the upper brain stem , left cerebellum and right basil ganglia"].toDS.toDF("text")).transform(data)
Results
| index | relations | entity1 | entity1_begin | entity1_end | chunk1 | entity2 | entity2_end | entity2_end | chunk2 | confidence |
|-------|-----------|-----------------------------|---------------|-------------|------------|-----------------------------|-------------|-------------|---------------|------------|
| 0 | 1 | Direction | 35 | 39 | upper | Internal_organ_or_component | 41 | 50 | brain stem | 0.9999989 |
| 1 | 0 | Direction | 35 | 39 | upper | Internal_organ_or_component | 59 | 68 | cerebellum | 0.99992585 |
| 2 | 0 | Direction | 35 | 39 | upper | Internal_organ_or_component | 81 | 93 | basil ganglia | 0.9999999 |
| 3 | 0 | Internal_organ_or_component | 41 | 50 | brain stem | Direction | 54 | 57 | left | 0.999811 |
| 4 | 0 | Internal_organ_or_component | 41 | 50 | brain stem | Direction | 75 | 79 | right | 0.9998203 |
| 5 | 1 | Direction | 54 | 57 | left | Internal_organ_or_component | 59 | 68 | cerebellum | 1.0 |
| 6 | 0 | Direction | 54 | 57 | left | Internal_organ_or_component | 81 | 93 | basil ganglia | 0.97616416 |
| 7 | 0 | Internal_organ_or_component | 59 | 68 | cerebellum | Direction | 75 | 79 | right | 0.953046 |
| 8 | 1 | Direction | 75 | 79 | right | Internal_organ_or_component | 81 | 93 | basil ganglia | 1.0 |
Model Information
| Model Name: | redl_bodypart_direction_biobert |
| Compatibility: | Spark NLP 2.7.3+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
Data Source
Trained on an internal dataset.
Benchmarking
Relation Recall Precision F1 Support
0 0.856 0.873 0.865 153
1 0.986 0.984 0.985 1347
Avg. 0.921 0.929 0.925