maharshisuchak / spacy-tutorial

Basic Terms

• Tokenization: Segmenting text into words, punctuations marks etc.
• Part-of-speech: (POS) Tagging Assigning word types to tokens, like verb or noun.
• Dependency Parsing: Assigning syntactic dependency labels, describing the relations between individual tokens, like subject or object.
• Lemmatization: Assigning the base forms of words. For example, the lemma of "was" is "be", and the lemma of "rats" is "rat".
• Named Entity Recognition (NER): Labelling named "real-world" objects, like persons, companies or locations.
• Similarity: Comparing words, text spans and documents and how similar they are to each other.
• Sentence Boundary Detection (SBD): Finding and segmenting individual sentences.
• Text Classification: Assigning categories or labels to a whole document, or parts of a document.
• Rule-based Matching: Finding sequences of tokens based on their texts and linguistic annotations, similar to regular expressions.
• Training: Updating and improving a statistical model's predictions.
• Serialization: Saving objects to files or byte strings.

Installing the Library on Linux/Unix

• sudo pip install spacy
• sudo python -m spacy download en
• sudo python -m spacy download fr

Installing On Windows using Conda

• conda install tqdm
• conda install -c conda-forge spacy /conda install spacy
• python -m spacy download en

• • with cmd administrator

Installing using Conda

• conda install -c conda-forge spacy
• sudo python -m spacy download en
• sudo python -m spacy download fr

For Download the Models of other languages

• sudo python -m spacy download de
• sudo python -m spacy download es
• sudo python -m spacy download xx

Installing On Windows using Conda

• conda config-add channel conda-forge
• conda update anaconda
• conda install tqdm
• conda install -c conda-forge spacy
• sudo python -m spacy download en