Essence

Essence is a Natural Language Processing (NLP) and Text Summarization library for Elixir. The work is currently in very early stages.

ToDo

• Tokenization (Basic, done)
• Sentence Detection and Chunking (Basic, done)
• Vocabulary (Basic, done)
• Documents (Draft, done)
• Readability (ARI done, SMOG in progress, FC, GF, DC, CL todo)
• Corpora
• Bi-Grams
• Tri-Grams
• n-Grams
• Frequency Measures
• Time-Series Documents
• Dispersion
• Similarity Measures
• Part of Speech Tagging
• Sentiment Analysis
• Classification
• Summarization
• Document Hierarchies

Installation

If available in Hex, the package can be installed as:

1. Add essence to your list of dependencies in mix.exs:

```elixir
def deps do
  [{:essence, "~> 0.1.0"}]
end
```

Examples

In the following examples we will use test/genesis.txt, which is a copy of the book of genesis from the King James Bible (http://www.gutenberg.org/ebooks/8001.txt.utf-8).

We provide a convenience method for reading the plain text of the book of genesis into Essence via the method Essence.genesis/1

Let's first create a document from the text:

iex> document = Essence.Document.from_text Essence.genesis

We can see that the text contains 1,533 paragraphs, 1,663 sentences and 44,741 tokens.

iex> document |> Essence.Document.enumerate_tokens |> Enum.count
iex> document |> Essence.Document.paragraphs |> Enum.count
iex> document |> Essence.Document.sentences |> Enum.count

What might the first sentence of genesis be?

iex> Essence.Document.sentence document, 0

Now let's compute the frequency distribution for tokens in the book of genesis:

iex> fd = Essence.Vocabulary.freq_dist document

What is the vocabulary of this text?

iex> vocabulary = Essence.Vocabulary.vocabulary document

or alternatively we can use the frequency distribution for the equivalent expression:

iex> vocabulary = Map.keys fd

What might the top 10 most frequent tokens be?

iex> vocabulary |> Enum.sort_by( fn(x) -> Map.get(fd, x) end, &>=/2 ) |> Enum.slice(1, 10)
["and", "the", "of", ".", "And", ":", "his", "he", "to", ";"]

Next, we can compute the lexical richness of the text:

iex> Essence.Vocabulary.lexical_richness document
16.74438622754491