#!/usr/bin/env python
# coding: utf-8

# In[1]:


import spacy


# In[2]:


nlp = spacy.load("en_core_web_sm")
text = "West Chestertenfieldville was referenced in Mr. Deeds."


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doc = nlp(text)


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for ent in doc.ents:
    print (ent.text, ent.label_)


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ruler = nlp.add_pipe("entity_ruler")


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patterns = [
            {"label": "GPE", "pattern": "West Chestertenfieldville"}
]
ruler.add_patterns(patterns)


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doc = nlp(text)
for ent in doc.ents:
    print (ent.text, ent.label_)


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nlp.analyze_pipes()


# In[9]:


nlp = spacy.load("en_core_web_sm")


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ruler = nlp.add_pipe("entity_ruler", before="ner")
patterns = [
            {"label": "GPE", "pattern": "West Chestertenfieldville"}
]
ruler.add_patterns(patterns)


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nlp.analyze_pipes()


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doc = nlp(text)


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for ent in doc.ents:
    print (ent.text, ent.label_)


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nlp3 = spacy.load("en_core_web_sm")


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ruler = nlp3.add_pipe("entity_ruler", before="ner")
patterns = [
            {"label": "GPE", "pattern": "West Chestertenfieldville"},
            {"label": "FILM", "pattern": "Mr. Deeds"}
]
ruler.add_patterns(patterns)


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doc = nlp3(text)


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for ent in doc.ents:
    print (ent.text, ent.label_)


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text = "This is a sample number (555) 555-5555."


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nlp = spacy.blank("en")
ruler = nlp.add_pipe("entity_ruler")


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patterns = [
            {"label": "PHONE_NUMBER",
             "pattern": [
                 {"ORTH": "("},
                {"SHAPE": "ddd"},
                 {"ORTH": ")"},
            {"SHAPE": "ddd"},
                 {"ORTH": "-", "OP": "?"},
                 {"SHAPE": "dddd"}
                                                  ]}
    
]
ruler.add_patterns(patterns)


# In[24]:


doc = nlp(text)
for ent in doc.ents:
    print (ent.text, ent.label_)


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