Description
A Part of Speech classifier predicts a grammatical label for every token in the input text. Implemented with an averaged perceptron architecture.
Predicted Entities
- ADJ
- ADP
- ADV
- AUX
- CCONJ
- DET
- NOUN
- NUM
- PART
- PRON
- PROPN
- PUNCT
- VERB
Live Demo Open in Colab Download Copy S3 URI
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols("sentence")\
.setOutputCol("token")
pos = PerceptronModel.pretrained("pos_talbanken", "sv")\
.setInputCols(["document", "token"])\
.setOutputCol("pos")
pipeline = Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
posTagger
])
example = spark.createDataFrame([["' Medicinsk bildtolk ' också skall fungera som hjälpmedel för läkaren att klarlägga sjukdomsbilden utan att patienten behöver säga ett ord ."]], ["text"])
result = pipeline.fit(example).transform(example)
val document_assembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = SentenceDetector()
.setInputCols("document")
.setOutputCol("sentence")
val tokenizer = Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val pos = PerceptronModel.pretrained("pos_talbanken", "sv")
.setInputCols(Array("document", "token"))
.setOutputCol("pos")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector,tokenizer , pos))
val data = Seq(" Medicinsk bildtolk " också skall fungera som hjälpmedel för läkaren att klarlägga sjukdomsbilden utan att patienten behöver säga ett ord .").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = [""' Medicinsk bildtolk ' också skall fungera som hjälpmedel för läkaren att klarlägga sjukdomsbilden utan att patienten behöver säga ett ord .""]
token_df = nlu.load('sv.pos.talbanken').predict(text)
token_df
Results
+---------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
|text |result |
+---------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
|' Medicinsk bildtolk ' också skall fungera som hjälpmedel för läkaren att klarlägga sjukdomsbilden utan att patienten behöver säga ett ord . |[PUNCT, ADJ, NOUN, PUNCT, ADV, AUX, VERB, SCONJ, NOUN, ADP, NOUN, PART, VERB, NOUN, ADP, SCONJ, NOUN, AUX, VERB, DET, NOUN, PUNCT]|
+---------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
Model Information
| Model Name: | pos_talbanken |
| Compatibility: | Spark NLP 2.7.5+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [pos] |
| Language: | sv |
Data Source
The model was trained on the Universal Dependencies data set.
Benchmarking
| | precision | recall | f1-score | support |
|--------------|-----------|--------|----------|---------|
| ADJ | 0.88 | 0.89 | 0.89 | 1826 |
| ADP | 0.96 | 0.96 | 0.96 | 2298 |
| ADV | 0.91 | 0.87 | 0.89 | 1528 |
| AUX | 0.91 | 0.93 | 0.92 | 1021 |
| CCONJ | 0.95 | 0.94 | 0.94 | 791 |
| DET | 0.92 | 0.95 | 0.93 | 1015 |
| INTJ | 1.00 | 0.33 | 0.50 | 3 |
| NOUN | 0.94 | 0.95 | 0.95 | 4711 |
| NUM | 0.98 | 0.96 | 0.97 | 357 |
| PART | 0.93 | 0.94 | 0.94 | 406 |
| PRON | 0.94 | 0.91 | 0.92 | 1449 |
| PROPN | 0.88 | 0.83 | 0.85 | 243 |
| PUNCT | 0.97 | 0.98 | 0.98 | 2104 |
| SCONJ | 0.86 | 0.82 | 0.84 | 491 |
| SYM | 0.50 | 1.00 | 0.67 | 1 |
| VERB | 0.90 | 0.90 | 0.90 | 2142 |
| accuracy | | | 0.93 | 20386 |
| macro avg | 0.90 | 0.89 | 0.88 | 20386 |
| weighted avg | 0.93 | 0.93 | 0.93 | 20386 |