Vocabulary

<iframe width="560" height="315" src="https://www.youtube.com/embed/BWsvqORbrpw" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

Vocabulary tokenization (also called word segmentation) is the problem of dividing a string of written language into its component words. Lettria enables you to quickly and easily extract the vocabulary as well as the part of speech to enable you to easily filter and analyze your document.

In order to extract the vocabulary from your document you can use the TextChunk class Vocabulary tool.

After you've installed the Lettria package on Python you'll need to import the library.

import lettria

Next you are going to need to include your personal API key which can be found

via the Lettria platform in the dashboard.

api_key = 'your personal API key'
nlp = lettria.NLP(api_key)

Now you will need to open your saved document. Be sure to add the name of

‘your file’ since it may differ from the name of my example file.

with open("example.txt", "r") as f:
	example_data = f.readlines()

Next, add the document to the NLP.

nlp.add_document(example_data)

In order to extract the vocabulary of the document use the following command:

vocabulary = nlp.vocabulary
vocabulary(filter_pos = None, lemma=False)

In the results you will have a list of the vocabulary, part of speech and lemma.

If you would like to filter out parts of speech such as ‘nouns’ you can change the filter criteria.

vocabulary = nlp.vocabulary
vocabulary(filter_pos = 'N', lemma=False)

Now you can see within the results all the vocabulary falling under ‘noun’ has been filtered out.

In order to save your results you can use the following command.

nlp.save_results(‘example_results')

And a json file with your results that can be used for further analysis will be saved.

import lettria

api_key = 'your personal API key'
nlp = lettria.NLP(api_key)

with open("example.txt", "r") as f:
	example_data = f.readlines()

nlp.add_document(example_data)

vocabulary = nlp.vocabulary
vocabulary(filter_pos = None, lemma=False)

vocabulary = nlp.vocabulary
vocabulary(filter_pos = 'N', lemma=False)

nlp.save_results(‘example_results')