Part of Speech for Vietnamese

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
  • X

Live Demo Open in Colab Download

How to use

document_assembler = DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

sentence_detector = SentenceDetector()
  .setInputCols(["document"])
  .setOutputCol("sentence")

pos = PerceptronModel.pretrained("pos_vtb", "vi")
  .setInputCols(["document", "token"])
  .setOutputCol("pos")

pipeline = Pipeline(stages=[
  document_assembler,
  sentence_detector,
  posTagger
])

example = spark.createDataFrame(pd.DataFrame({'text': ["Thắng sẽ tìm nghề mới cho Lan ."]}))

result = pipeline.fit(example).transform(example)
val document_assembler = DocumentAssembler()
        .setInputCol("text")
        .setOutputCol("document")

val sentence_detector = SentenceDetector()
        .setInputCols(["document"])
.setOutputCol("sentence")

val pos = PerceptronModel.pretrained("pos_vtb", "vi")
        .setInputCols(Array("document", "token"))
        .setOutputCol("pos")

val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, pos))

val result = pipeline.fit(Seq.empty["Thắng sẽ tìm nghề mới cho Lan ."].toDS.toDF("text")).transform(data)
import nlu
text = [""Thắng sẽ tìm nghề mới cho Lan .""]
token_df = nlu.load('vi.pos.vtb').predict(text)
token_df

Results

+-------------------------------+--------------------------------------------+
|text                           |result                                      |
+-------------------------------+--------------------------------------------+
|Thắng sẽ tìm nghề mới cho Lan .|[NOUN, X, VERB, NOUN, ADJ, ADP, NOUN, PUNCT]|
+-------------------------------+--------------------------------------------+

Model Information

Model Name: pos_vtb
Compatibility: Spark NLP 2.7.5+
License: Open Source
Edition: Official
Input Labels: [sentence, token]
Output Labels: [pos]
Language: vi

Data Source

The model was trained on the Universal Dependencies data set.

Benchmarking

|              | precision | recall | f1-score | support |
|--------------|-----------|--------|----------|---------|
| ADJ          | 0.58      | 0.49   | 0.53     | 738     |
| ADP          | 0.84      | 0.87   | 0.86     | 688     |
| AUX          | 0.79      | 0.95   | 0.87     | 132     |
| CCONJ        | 0.85      | 0.80   | 0.83     | 335     |
| DET          | 0.95      | 0.85   | 0.90     | 232     |
| INTJ         | 1.00      | 0.14   | 0.25     | 7       |
| NOUN         | 0.84      | 0.86   | 0.85     | 3838    |
| NUM          | 0.94      | 0.91   | 0.92     | 412     |
| PART         | 0.53      | 0.30   | 0.38     | 87      |
| PROPN        | 0.85      | 0.85   | 0.85     | 494     |
| PUNCT        | 0.97      | 0.99   | 0.98     | 1722    |
| SCONJ        | 0.99      | 0.98   | 0.98     | 122     |
| VERB         | 0.73      | 0.76   | 0.74     | 2178    |
| X            | 0.81      | 0.76   | 0.79     | 970     |
| accuracy     |           |        | 0.83     | 11955   |
| macro avg    | 0.83      | 0.75   | 0.77     | 11955   |
| weighted avg | 0.83      | 0.83   | 0.83     | 11955   |