Split Sentences in English Texts

Description

SentenceDetectorDL (SDDL) is based on a general-purpose neural network model for sentence boundary detection. The task of sentence boundary detection is to identify sentences within a text. Many natural language processing tasks take a sentence as an input unit, such as part-of-speech tagging, dependency parsing, named entity recognition or machine translation.

Live Demo Open in Colab Download Copy S3 URI

How to use

documenter = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentencerDL = SentenceDetectorDLModel\
.pretrained("sentence_detector_dl", "en") \
.setInputCols(["document"]) \
.setOutputCol("sentences")
sd_model = LightPipeline(PipelineModel(stages=[documenter, sentencerDL]))
sd_model.fullAnnotate("""John loves Mary.Mary loves Peter. Peter loves Helen .Helen loves John; Total: four people involved.""")

val documenter = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val model = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en")
.setInputCols(Array("document"))
.setOutputCol("sentence")
val pipeline = new Pipeline().setStages(Array(documenter, model))
val data = Seq("John loves Mary.Mary loves Peter. Peter loves Helen .Helen loves John; Total: four people involved.").toDF("text")
val result = pipeline.fit(data).transform(data)

import nlu

text = ["""John loves Mary.Mary loves Peter. Peter loves Helen .Helen loves John; Total: four people involved."""]
sent_df = nlu.load('sentence_detector.deep').predict(text, output_level='sentence')
sent_df

Results

+---+------------------------------+
| 0 | John loves Mary.             |
+---+------------------------------+
| 1 | Mary loves Peter             |
+---+------------------------------+
| 2 | Peter loves Helen .          |
+---+------------------------------+
| 3 | Helen loves John;            |
+---+------------------------------+
| 4 | Total: four people involved. |
+---+------------------------------+

Model Information

Data Source

Please visit the repo for more information https://github.com/dbmdz/deep-eos

Benchmarking

label  Accuracy  Recall   Prec   F1  
0      0.98      1.00     0.96   0.98