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.

In this model, we treated the sentence boundary detection task as a classification problem based on a paper {Deep-EOS: General-Purpose Neural Networks for Sentence Boundary Detection (2020, Stefan Schweter, Sajawel Ahmed) using CNN architecture. We also modified the original implemenation a little bit to cover broken sentences and some impossible end of line chars.

We are releasing two pretrained SDDL models: english and multilanguage that are trained on SETimes corpus (Tyers and Alperen, 2010) and Europarl. Wong et al. (2014) datasets.

Live Demo Open in Colab Download

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 result = pipeline.fit(Seq.empty["John loves Mary.Mary loves Peter. Peter loves Helen .Helen loves John; Total: four people involved."].toDS.toDF("text")).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

|    | Accuracy | Recall   | Prec     | F1   |
|---:|---------:|---------:|---------:|-----:|
|  0 | 0.98     | 1.00     | 0.96     | 0.98 |