Hands-On Machine Learning for Algorithmic Trading

Multi-language NLP

spaCy includes trained language models for English, German, Spanish, Portuguese, French, Italian, and Dutch, as well as a multi-language model for NER. Cross-language usage is straightforward since the API does not change.

We will illustrate the Spanish language model using a parallel corpus of TED Talk subtitles (see the GitHub repo for data source references). For this purpose, we instantiate both language models:

model = {}for language in ['en', 'es']:    model[language] = spacy.load(language)

We then read small corresponding text samples in each model:

text = {}path = Path('../data/TED')for language in ['en', 'es']:    file_name = path / 'TED2013_sample.{}'.format(language)    text[language] = file_name.read_text()

Sentence ...