Python NLP & spaCy Helper-spaCy code assistant

Example

```python import spacy nlp = spacy.load('en_core_web_sm') doc = nlp('Apple is looking at buying U.K. startup for $1 billion') for token in doc: print(token.text, token.lemma_, token.pos_, token.dep_) ```

Scenario

Extracting linguistic features from text such as lemmatization, part-of-speech tagging, and syntactic dependencies for downstream tasks like information extraction or text classification.

Example

```python import spacy nlp = spacy.load('en_core_web_sm') doc = nlp('Barack Obama was born on August 4, 1961, in Honolulu, Hawaii.') for ent in doc.ents: print(ent.text, ent.label_) ```

Scenario

Identifying and categorizing entities in text, such as names of people, organizations, locations, dates, etc., which is crucial for tasks like automated content tagging, entity linking, and knowledge graph construction.

Example

```python import spacy nlp = spacy.load('en_core_web_sm') doc = nlp('Autonomous cars shift insurance liability toward manufacturers.') for chunk in doc.noun_chunks: print(chunk.text, chunk.root.text, chunk.root.dep_, chunk.root.head.text) ```

Scenario

Understanding the grammatical structure of a sentence to analyze the relationships between words, which is useful for tasks like syntactic analysis, information retrieval, and text generation.