Dependency Parsing: A Powerful Technique for Understanding the Structure of Sentences

When you read a sentence, you probably don’t think much about how each word relates to the others. However, understanding the relationships between words is essential for many natural language processing tasks, such as named entity recognition, sentiment analysis, and machine translation. One technique for analyzing the relationships between words in a sentence is called dependency parsing.

What is Dependency Parsing?

Dependency Parsing refers to the process of examining the grammatical arrangement of a sentence to identify connected words and their associated relationship types. The output of a dependency parser is a directed graph that represents the dependencies between the words in a sentence. In this graph, the words are represented as nodes, and the dependencies between them are represented as directed edges.

To perform dependency parsing, the parser needs to know the parts of speech (POS) of each word in the sentence. It uses this information to determine the syntactic relationships between the words. For example, it can identify which words are subjects, objects, or modifiers of other words in the sentence.

Types of Dependencies

There are several types of dependencies that can exist between words in a sentence. Here are a few examples:

Subject: The subject of a sentence is typically the noun or pronoun that performs the action of the sentence. In the sentence “John ate a sandwich,” “John” is the subject.
Object: The object of a sentence is the noun or pronoun that is affected by the action of the sentence. In the sentence “John ate a sandwich,” “sandwich” is the object.
Modifier: A modifier is a word or phrase that describes another word in the sentence. For example, in the sentence “The big, black dog ran down the street,” “big” and “black” are modifiers of “dog.”

Why Use Dependency Parsing?

Dependency parsing is a powerful technique for understanding the structure of sentences. Here are a few reasons why you might use it:

Named entity recognition: Dependency parsing can help you identify named entities in a sentence, such as people, places, and organizations. For example, you might use it to identify the subject of a sentence that mentions a person’s name.
Sentiment analysis: Dependency parsing can help you determine the sentiment of a sentence by identifying the subject and object of the sentence. For example, if the subject is positive and the object is negative, the overall sentiment of the sentence might be negative.
Machine translation: Dependency parsing can help you generate more accurate translations by providing information about the relationships between words in the source and target languages.

Tools for Dependency Parsing

There are many tools available for performing dependency parsing, including:

Stanford Parser: A popular parser developed by Stanford University.
spaCy: A Python library that includes a dependency parser.
CoreNLP: A suite of natural language processing tools developed by Stanford University.

Implementing Dependency Parsing in Python using Spacy

For implementing Dependency Parsing, we would make use of the spaCy module in Python.

Dependency _Parse

In [2]:

#Import libraries
import re
import pandas as pd
import spacy
spacy.cli.download("en_core_web_sm")
from spacy import displacy
nlp = spacy.load('en_core_web_sm')

✔ Download and installation successful
You can now load the package via spacy.load('en_core_web_sm')

Creating a dependency parse tree using spaCy¶

In [45]:

#Sentences
text1 = 'I trained for more than two hours straight to perfect my jump shot.'
text2 = 'The rival team seemed to be having more fun.'

In [46]:

# Process each sentence and generate the dependency parse tree
doc1 = nlp(text1)
doc2 = nlp(text2)

In [47]:

# Print dependency trees
for token in doc1:
    print(token.text, token.dep_, token.head.text, token.head.pos_,
          [child for child in token.children])

I nsubj trained VERB []
trained ROOT trained VERB [I, for, perfect, .]
for prep trained VERB [straight]
more amod two NUM []
than quantmod two NUM []
two nummod hours NOUN [more, than]
hours npadvmod straight ADV [two]
straight pcomp for ADP [hours]
to aux perfect VERB []
perfect advcl trained VERB [to, shot]
my poss jump NOUN []
jump compound shot VERB [my]
shot advcl perfect VERB [jump]
. punct trained VERB []

In [48]:

for token in doc2:
    print(token.text, token.dep_, token.head.text, token.head.pos_,
          [child for child in token.children])

The det team NOUN []
rival amod team NOUN []
team nsubj seemed VERB [The, rival]
seemed ROOT seemed VERB [team, having, .]
to aux having VERB []
be aux having VERB []
having xcomp seemed VERB [to, be, fun]
more amod fun NOUN []
fun dobj having VERB [more]
. punct seemed VERB []

In [52]:

# Visualize the dependency parse tree for each sentence
displacy.render(doc1, style='dep', options={'compact': True, 'distance': 75})
displacy.render(doc2, style='dep', options={'compact': True, 'distance': 80})

In [ ]:

Conclusion

Dependency parsing is a powerful technique for understanding the structure of sentences. By analyzing the relationships. Dependency parsing is a powerful technique for understanding the structure of sentences. By analyzing the relationships between words in a sentence, you can gain insights into the meaning of the sentence and perform more accurate natural language processing tasks. Whether you are working on named entity recognition, sentiment analysis, or machine translation, dependency parsing can help you achieve better results.