Stanford versus Apache Tokenization

Stanford CoreNLP and Apache OpenNLP are two of the most widely used tokenization methods, or natural language processing toolkits. What are the differences between the two?

1. In addition to tokenization (the division of text into separate words), both perform sentence segmentation, named entity recognition, and co-reference resolution. NER is the identification of entities such as places, dollar values, personal names, and organizations in unstructured text. Co-reference resolution involves finding every reference to an entity in a source document. Unlike Apache, Stanford also accounts for lemmatization (the various inflections of a word - see the Tip of the Night for April 28, 2019).

2. Apache works faster than Stanford, and will work with larger data sets than Stanford can.

3. Stanford requires fewer lines of code.

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