Spark NLP for Data Scientists

?Spell Checking is a very important task in any NLP pipeline that needs to deal with noisy and incorrect data. In addition to text generated by Optical Character Recognition (OCR), social media interactions like tweets, instant messaging, blog posts, or any other user generated text, content will cause problems. Being able to rely on correct data, without spelling problems reduces vocabulary sizes at different stages in the pipeline, and improves the performance of all the models in the pipeline.

Spell Checkers can recommend corrections on three levels: subword level, word level and sentence level. Spark-NLP’s ContextSpellChecker annotator, uses contextual information to both detect errors and produce the best corrections.

?Spell Checking is a very important task in any NLP pipeline that needs to deal with noisy and incorrect data. In addition to text generated by Optical Character Recognition (OCR), social media interactions like tweets, instant messaging, blog posts, or any other user generated text, content will cause problems. Being able to rely on correct data, without spelling problems reduces vocabulary sizes at different stages in the pipeline, and improves the performance of all the models in the pipeline.

Spell Checkers can recommend corrections on three levels: subword level, word level and sentence level. Spark-NLP’s ContextSpellChecker annotator, uses contextual information to both detect errors and produce the best corrections.

?Spell Checking is a very important task in any NLP pipeline that needs to deal with noisy and incorrect data. In addition to text generated by Optical Character Recognition (OCR), social media interactions like tweets, instant messaging, blog posts, or any other user generated text, content will cause problems. Being able to rely on correct data, without spelling problems reduces vocabulary sizes at different stages in the pipeline, and improves the performance of all the models in the pipeline.

Spell Checkers can recommend corrections on three levels: subword level, word level and sentence level. Spark-NLP’s ContextSpellChecker annotator, uses contextual information to both detect errors and produce the best corrections.

?Spell Checking is a very important task in any NLP pipeline that needs to deal with noisy and incorrect data. In addition to text generated by Optical Character Recognition (OCR), social media interactions like tweets, instant messaging, blog posts, or any other user generated text, content will cause problems. Being able to rely on correct data, without spelling problems reduces vocabulary sizes at different stages in the pipeline, and improves the performance of all the models in the pipeline.

Spell Checkers can recommend corrections on three levels: subword level, word level and sentence level. Spark-NLP’s ContextSpellChecker annotator, uses contextual information to both detect errors and produce the best corrections.

?Learning Objectives:

1. Understand how to check spelling using NorvigSweeting annotators.

2. Understand the difference between NorvigSweetingApproach and NorvigSweetingModel.

3. Customize the use of these annotators by setting their parameters.

?Learning Objectives:

1. Understand how to check spelling using SymmetricDelete annotators.

2. Understand the difference between SymmetricDeleteApproach and SymmetricDeleteModel.

3. Customize the use of these annotators by setting their parameters.

? Learning Objectives:

With this DateMatcher and MultiDateMatcher Notebook, you will be able to:

1. Know the differences between DateMatcher and MultiDateMatcher,

2. Extract date from text,

3. Deal with relative dates,

4. Change input/output date formats,

5. Set missing day in date without day,

6. Extract dates in different languages.

?Learning Objectives:

1. Understand how to use NGramGenerator.

2. Become familiar with the parameters and options available for the NGramGenerator.

? Learning Objectives:

1. Understand the process of reducing inflected words to their base forms to obtain the lemmas.

2. Be able to train custom LemmatizerModel annotators.

3. Become confortable with creating pipelines to preprocess texts with Lemmatizer and LemmatizerModel.

? Learning Objectives:

1. Understand how extract the base form of the words by removing affixes from them.

2. Become comfortable using the different parameters of the annotator.

? Learning Objectives:

1. Understand how SentenceDetectorDL algorithm works.

2. Understand how SentenceDetectorDL follows an unsupervised approach which builds upon features extracted from the text.

3. Become comfortable using the different parameters of the annotator.

?Learning Objectives:

• Understand how to clean tokens by making use of this annotator.

• Become comfortable using the different parameters of the annotator.

?Learning Objectives:

1. Understand how to drop stop words from the input sequences.

2. Become comfortable using the different parameters of the annotator.

3. How to use pretrained StopWordsCleaner models.

?Learning Objectives:

1. Understand how we can normalize raw text from tagged text eg: scrapped web pages, xml documents etc.

2. Become comfortable using the different parameters of the annotator.

? Learning Objectives:

1. Understand how to use Tokenizer.

2. Become comfortable using the different parameters of the Tokenizer.

?Learning Objectives:

1. Understand how different regex patterns split sequences of words in different ways.

2. Understand the difference between the regex tokenizer and regular tokenizer.

3. Become comfortable using the different parameters of the annotator.

?Learning Objectives:

1. Understand how to split chunks into tokens in different ways.

2. Become comfortable using the different parameters of the annotator.

?Learning Objectives:

1. Understand how it reconstructs a DOCUMENT type annotation from tokens.

2. Become comfortable using the different parameters of the annotator.

? Learning Objectives:

1. Understand the meaning of Keyword Extraction, namely being the process of automatically extracting the most important keywords from a text document.

2. Understand how YakeKeywordExtraction follows an unsupervised approach which builds upon features extracted from the text.

3. Become comfortable using the different parameters of the annotator - most parameters will help define:

   • total number of keywords to be selected,

   • minimum or maximum words in a keyword,

   • list of stopwords.

? Learning Objectives:

1. Find occurrences of regular expression (regex) patterns in text

2. Set one or more regex rules and assign an identifier for each regex rule

3. Create and use external regex rules file

4. Change the matching strategy of RegexMatcher