Introduction to Spacy Dependency Parsing with Pandas Dataframe

Spacy is a popular Natural Language Processing (NLP) library that provides high-performance, streamlined processing of text data. One of its key features is dependency parsing, which allows us to analyze the grammatical structure of sentences and identify relationships between words.

In this article, we will explore how to use Spacy’s dependency parser to extract noun-adjective pairs from a pandas dataframe. We will delve into the technical details of Spacy’s parsing process, discuss common pitfalls, and provide guidance on how to optimize your code for better performance.

Installing Spacy

Before we begin, make sure you have installed Spacy using pip:

!pip install spacy
!python -m spacy download en_core_web_lg

This will download the English language model, which is a pre-trained model that can be used for various NLP tasks.

Understanding Spacy’s Dependency Parser

Spacy’s dependency parser works by analyzing the grammatical structure of sentences and identifying relationships between words. The parser uses a combination of machine learning algorithms and linguistic rules to assign each word in a sentence a role, such as “nsubj” (nominal subject), “acomp” (adjectival complement), or “neg” (negation).

In Spacy, the dependency parse tree is represented as a graph data structure, where each node represents a word in the sentence and its corresponding roles. The edges between nodes represent the relationships between words.

Parsing with Spacy

To use Spacy’s dependency parser, we need to load the en_core_web_lg model and create a document object:

import spacy

nlp = spacy.load("en_core_web_lg")

doc = nlp("This is an example sentence.")

We can then access the parse tree using the doc.ents attribute, which returns a list of entity objects, each representing a named entity in the sentence:

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

Working with Pandas Dataframes

To work with Spacy’s dependency parser on a pandas dataframe, we need to apply the parsing process to each row in the dataframe. We can do this using the apply method, which applies a function to each element in a series or DataFrame.

import pandas as pd

# Create a sample dataframe
df = pd.DataFrame({
    "Text": ["This is an example sentence.", "Another sentence for testing."]
})

def find_sentiment(text):
    # Load the Spacy model and create a document object
    doc = nlp(text)
    
    # Initialize variables to store the noun-adjective pairs
    A = "999999"
    M = "999999"
    rule3_pairs = []
    
    # Iterate over each token in the sentence
    for token in doc:
        children = token.children
        
        # Check if the token is a nominal subject (nsubj)
        if token.dep_ == "nsubj" and not token.is_stop:
            if token.idx in df.ner_heads:
                A = df.ner_heads[token.idx].text
            else:
                A = token.text
        
        # Check if the token is an adjectival complement (acomp)
        if token.dep_ == "acomp" and not token.is_stop:
            M = token.text
        
        # Check if the token is a modal auxiliary (aux)
        if token.dep_ == "aux" and token.tag_ == "MD":
            neg_prefix = "not"
            add_neg_pfx = True
        # Check if the token is a negation (neg)
        elif token.dep_ == "neg":
            neg_prefix = token.text
            add_neg_pfx = True
        
        # Update the noun-adjective pairs based on the token's role
        if add_neg_pfx and M != "999999":
            M = neg_prefix + " " + M
        if A != "999999" and M != "999999":
            rule3_pairs.append((A, M))
    
    # Return the list of noun-adjective pairs
    return rule3_pairs

# Apply the parsing process to each row in the dataframe
df["three_tuples"] = df["Text"].apply(find_sentiment)

print(df.head())

Optimizing Performance

When working with large datasets, it’s essential to optimize performance to avoid slowing down your code. Here are some tips to improve the performance of your Spacy-dependent parsing pipeline:

• Use the nlp object in batch mode: Instead of creating a new document object for each row in your dataframe, use the nlp object in batch mode to process multiple documents at once.

def find_sentiment(text):
    # Load the Spacy model and create a document object
    doc = nlp(text)
    
    # Initialize variables to store the noun-adjective pairs
    A = "999999"
    M = "999999"
    rule3_pairs = []
    
    # Iterate over each token in the sentence
    for token in doc:
        children = token.children
        
        # ...

• Use the doc.tokens attribute: Instead of iterating over each token in the document using a loop, use the doc.tokens attribute to access the tokens directly.

def find_sentiment(text):
    # Load the Spacy model and create a document object
    doc = nlp(text)
    
    # Initialize variables to store the noun-adjective pairs
    A = "999999"
    M = "999999"
    rule3_pairs = []
    
    # Iterate over each token in the sentence using doc.tokens
    for token in doc.tokens:
        children = token.children
        
        # ...

• Use caching: If you’re performing expensive computations, consider using caching to store intermediate results and avoid recalculating them.

def find_sentiment(text):
    # Load the Spacy model and create a document object
    doc = nlp(text)
    
    # Initialize variables to store the noun-adjective pairs
    A = "999999"
    M = "999999"
    rule3_pairs = []
    
    # Iterate over each token in the sentence
    for token in doc:
        children = token.children
        
        # ...

Conclusion

Spacy’s dependency parser is a powerful tool for analyzing the grammatical structure of sentences and identifying relationships between words. By understanding how to use Spacy’s parser on a pandas dataframe, you can unlock valuable insights into your text data.

Remember to optimize performance by using batch mode, accessing tokens directly, and caching intermediate results. With these tips and techniques, you’ll be able to extract noun-adjective pairs from your text data with ease.

Last modified on 2023-07-20