Spell Checkers can recommend corrections on the word level.

Besides Stemming or Lemmatizing, there still might be a need to edit words to move to more default words.

Lemmatization usually refers to rewriting a word to its base form (lemma) properly.

Stemming refers to a crude heuristic process that chops off the ends of words in the hope that words with the same meaning become words with the same syntax.

A Dependency Parser extracts a dependency graph from a sentence. In the graph the grammatical structure, like subject and object, and relationships between words represented by Dependency tags.

The syntactic function of a word, like Noun or Verb, is defined by the Part-of-Speech (POS tags) and is based on the context.

Assigning additional morphological information clarifies the grammatical meaning of a word, additionally to the syntax.

The goal of tokenization is a vocabulary. Word-based tokenizers have larger vocabularies with more Out-Of-Vocabulary (OOV) words than sub-word vocabularies.

Many NLP tasks make use of intensive matrix calculations, for which word id’s are used, rather than words. For this, raw text is split up into tokens that represent (sub)words.

Programmatically build training datasets by defining heuristic rules which are used in functions for labeling training data.

Boost your performance by creating data out of data, instead of new data.

Creating training data is a labor-intensive task. Fine-tune the training data definition yourself and then scale-up by outsourcing to remote workers.

Gold data contains the ground truth. Re-use available resources, but be careful that the dataset matches your purpose.

Use an annotation tool that benefits from active learning to enforce a robust annotion process and balanced annotations.

Nobody wants to do the manual labor of tagging. Everybody wants to build language models with annotated training data.

Extracting text and transforming it to qualitative data is challenging when you have the output format (PDF or image) and need to reduce it to the source format of textual data.

The lack of a Corpus or API requires you to scrape your textual data or files from the web. Overcome the challenges of IP-blocking, cookie walls, request headers and js-websites.

An API serves as the interface between different applications. The requestor automatically gets access to data, with the benefit that the source doesn’t have to know how the other system exactly works. 

A Corpus is a language resource consisting of a structured set of documents and additional information. It incorporates pre-processed documents and their meta-data, which might be the output of other NLP tasks. 

Structured data implies ready-to-use data, but you have to interpret it’s scheme.

Typing your own text gives you more confidence when testing your code or a demo.