Chapter 6: Syntax and Parsing
6.3 Dependency Parsing
Dependency parsing is a crucial task in Natural Language Processing that involves the analysis of the grammatical structure of a sentence, with particular attention to the dependencies between the words. This task is often used in many NLP applications, including machine translation, sentiment analysis, question answering systems, and information extraction.
Dependancy parsing is performed by using a parsing algorithm to analyze a sentence and identify the relationships between its words. The algorithm takes into account the parts of speech of each word, as well as the structure of the sentence, in order to generate a dependency tree that represents the relationships between the words. This tree can then be used to extract information from the sentence, such as the subject, object, and verb.
There are many different parsing algorithms available, each with its own strengths and weaknesses. Some of the most commonly used algorithms include the Stanford Parser, the Berkeley Parser, and the MaltParser. Each of these algorithms has its own unique approach to dependency parsing, and researchers continue to develop new algorithms as well.
While dependency parsing is a complex task, it is a critical component of many NLP applications. By understanding the relationships between words in a sentence, we can extract important information and insights from text data. In the next section, we will delve further into the details of dependency parsing and explore how it is used in practice.
6.3.1 What is Dependency Parsing?
Dependency Parsing is an important process in Natural Language Processing (NLP), which involves the analysis of a sentence in order to represent its grammatical structure. This analysis aims to define the grammatical relationships between words in a sentence and represents these relationships in the form of a directed graph.
The graph is constructed in such a way that nodes represent individual words and edges represent the grammatical relationships between the words. By analyzing the grammatical structure of a sentence, we can gain insights into the meaning and intent behind the text. These insights can be useful in a wide range of applications, from language translation to sentiment analysis and beyond.
For instance, consider the sentence: "The cat is sitting on the mat." In this sentence, "sitting" is the main (or 'head') verb. "The cat" is the subject of this verb, and "on the mat" is where the cat is sitting. So, the relationships would be something like this:
- "cat" -> "sitting" (the cat is doing the sitting)
- "on the mat" -> "sitting" (sitting where? on the mat)
Each of these relationships is a dependency, and together they form a dependency parse of the sentence.
6.3.2 Why is Dependency Parsing Important?
Understanding the grammatical structure of a sentence is crucial for comprehending its meaning. For example, consider the following sentences: "Alice saw Bob" and "Bob saw Alice". Both sentences contain the same words, but their meanings differ due to their grammatical structures. Dependency Parsing is a technique that helps us understand the structure of sentences, allowing us to comprehend their meanings.
Dependency Parsing is critical in machine translation applications, where the correct translation of a sentence depends on its grammatical structure. For example, in languages such as German and Hindi, the verb often appears at the end of a sentence. Therefore, we must understand the entire sentence's grammatical structure before we can begin translating. This is why Dependency Parsing is essential in such applications.
Dependency Parsing can be used in natural language processing tasks such as sentiment analysis and named entity recognition, where understanding the structure of a sentence can aid in identifying the sentiment or named entities.
6.3.3 Dependency Parsing with SpaCy
SpaCy library has built-in support for dependency parsing. Here's a simple example:
import spacy
# Load English tokenizer, tagger, parser, NER and word vectors
nlp = spacy.load('en_core_web_sm')
# Process whole documents
text = ("The cat is sitting on the mat.")
doc = nlp(text)
# Analyze syntax
for token in doc:
print(f"{token.text} <--{token.dep_}-- {token.head.text}")
This code will output:
The <--det-- cat
cat <--nsubj-- sitting
is <--aux-- sitting
sitting <--ROOT-- sitting
on <--prep-- sitting
the <--det-- mat
mat <--pobj-- on
. <--punct-- sitting
In this output, ROOT
denotes the main verb or action in the sentence. The abbreviations like det
, nsubj
, aux
, etc., represent the type of dependency relation.
6.3.4 Visualizing Dependencies
SpaCy also provides a way to visualize these dependencies using displaCy:
from spacy import displacy
# Visualize the dependency tree
displacy.serve(doc, style='dep')
This will start a web server and display a webpage with the visualized dependency tree.
6.3.5 Practical Considerations
Dependency Parsing is a complex task and has its challenges:
Ambiguity:
Some sentences can have multiple valid dependency parses. For instance, "I saw the man with the telescope." Could mean that I used a telescope to see the man (I -> saw -> man -> with -> telescope), or it could mean that I saw a man who had a telescope (I -> saw -> (man with telescope)). Determining the correct parse requires understanding the context and sometimes even world knowledge.
Long Sentences
Long sentences can be challenging to work with, especially when they contain complex grammatical structures that require sophisticated dependency parsing algorithms. These algorithms can be computationally intensive, which can cause processing delays and slow down the overall performance of a system.
However, despite these challenges, long sentences can also be incredibly rewarding to work with, as they often contain rich and nuanced ideas that cannot be conveyed in shorter, simpler sentences. By taking the time to carefully analyze and parse long sentences, researchers and developers can gain a deeper understanding of complex ideas and unlock new insights that can drive innovation and progress.
Therefore, while it may be tempting to avoid long sentences altogether, it is important to recognize their value and invest the necessary time and resources to work with them effectively.
Errors in Earlier Stages
One important aspect to consider when it comes to Dependency Parsing is its reliance on earlier stages of NLP such as tokenization and POS tagging. It is crucial to ensure that these earlier stages are accurate since any errors in them can potentially lead to significant errors in the parsing stage.
In fact, one might argue that the accuracy of the entire NLP process is only as good as its weakest link, emphasizing the importance of paying close attention to every stage of the process. Without accurate tokenization and POS tagging, the parsing stage can be rendered ineffective, which highlights the need for proper quality control throughout the entire NLP pipeline.
Despite these challenges, modern dependency parsers like the one in SpaCy achieve high accuracy and are fast enough for many practical applications.
6.3.6 Advanced Techniques
In addition to the basic techniques for dependency parsing that were discussed earlier, there are more advanced techniques available. These techniques utilize machine learning algorithms to predict the relationships between words in a sentence.
One such technique is graph-based parsing. This method constructs a complete graph of all possible dependencies between words in a sentence. Once the graph is built, an algorithm is used to determine the most probable tree structure within the graph.
Another advanced technique is transition-based parsing. This method builds the dependency tree in a step-by-step manner by predicting one dependency at a time.
Although these techniques are not covered in this introduction, they are important areas of research in the field of NLP and can be explored further if you wish to deepen your understanding of this subject.
6.3 Dependency Parsing
Dependency parsing is a crucial task in Natural Language Processing that involves the analysis of the grammatical structure of a sentence, with particular attention to the dependencies between the words. This task is often used in many NLP applications, including machine translation, sentiment analysis, question answering systems, and information extraction.
Dependancy parsing is performed by using a parsing algorithm to analyze a sentence and identify the relationships between its words. The algorithm takes into account the parts of speech of each word, as well as the structure of the sentence, in order to generate a dependency tree that represents the relationships between the words. This tree can then be used to extract information from the sentence, such as the subject, object, and verb.
There are many different parsing algorithms available, each with its own strengths and weaknesses. Some of the most commonly used algorithms include the Stanford Parser, the Berkeley Parser, and the MaltParser. Each of these algorithms has its own unique approach to dependency parsing, and researchers continue to develop new algorithms as well.
While dependency parsing is a complex task, it is a critical component of many NLP applications. By understanding the relationships between words in a sentence, we can extract important information and insights from text data. In the next section, we will delve further into the details of dependency parsing and explore how it is used in practice.
6.3.1 What is Dependency Parsing?
Dependency Parsing is an important process in Natural Language Processing (NLP), which involves the analysis of a sentence in order to represent its grammatical structure. This analysis aims to define the grammatical relationships between words in a sentence and represents these relationships in the form of a directed graph.
The graph is constructed in such a way that nodes represent individual words and edges represent the grammatical relationships between the words. By analyzing the grammatical structure of a sentence, we can gain insights into the meaning and intent behind the text. These insights can be useful in a wide range of applications, from language translation to sentiment analysis and beyond.
For instance, consider the sentence: "The cat is sitting on the mat." In this sentence, "sitting" is the main (or 'head') verb. "The cat" is the subject of this verb, and "on the mat" is where the cat is sitting. So, the relationships would be something like this:
- "cat" -> "sitting" (the cat is doing the sitting)
- "on the mat" -> "sitting" (sitting where? on the mat)
Each of these relationships is a dependency, and together they form a dependency parse of the sentence.
6.3.2 Why is Dependency Parsing Important?
Understanding the grammatical structure of a sentence is crucial for comprehending its meaning. For example, consider the following sentences: "Alice saw Bob" and "Bob saw Alice". Both sentences contain the same words, but their meanings differ due to their grammatical structures. Dependency Parsing is a technique that helps us understand the structure of sentences, allowing us to comprehend their meanings.
Dependency Parsing is critical in machine translation applications, where the correct translation of a sentence depends on its grammatical structure. For example, in languages such as German and Hindi, the verb often appears at the end of a sentence. Therefore, we must understand the entire sentence's grammatical structure before we can begin translating. This is why Dependency Parsing is essential in such applications.
Dependency Parsing can be used in natural language processing tasks such as sentiment analysis and named entity recognition, where understanding the structure of a sentence can aid in identifying the sentiment or named entities.
6.3.3 Dependency Parsing with SpaCy
SpaCy library has built-in support for dependency parsing. Here's a simple example:
import spacy
# Load English tokenizer, tagger, parser, NER and word vectors
nlp = spacy.load('en_core_web_sm')
# Process whole documents
text = ("The cat is sitting on the mat.")
doc = nlp(text)
# Analyze syntax
for token in doc:
print(f"{token.text} <--{token.dep_}-- {token.head.text}")
This code will output:
The <--det-- cat
cat <--nsubj-- sitting
is <--aux-- sitting
sitting <--ROOT-- sitting
on <--prep-- sitting
the <--det-- mat
mat <--pobj-- on
. <--punct-- sitting
In this output, ROOT
denotes the main verb or action in the sentence. The abbreviations like det
, nsubj
, aux
, etc., represent the type of dependency relation.
6.3.4 Visualizing Dependencies
SpaCy also provides a way to visualize these dependencies using displaCy:
from spacy import displacy
# Visualize the dependency tree
displacy.serve(doc, style='dep')
This will start a web server and display a webpage with the visualized dependency tree.
6.3.5 Practical Considerations
Dependency Parsing is a complex task and has its challenges:
Ambiguity:
Some sentences can have multiple valid dependency parses. For instance, "I saw the man with the telescope." Could mean that I used a telescope to see the man (I -> saw -> man -> with -> telescope), or it could mean that I saw a man who had a telescope (I -> saw -> (man with telescope)). Determining the correct parse requires understanding the context and sometimes even world knowledge.
Long Sentences
Long sentences can be challenging to work with, especially when they contain complex grammatical structures that require sophisticated dependency parsing algorithms. These algorithms can be computationally intensive, which can cause processing delays and slow down the overall performance of a system.
However, despite these challenges, long sentences can also be incredibly rewarding to work with, as they often contain rich and nuanced ideas that cannot be conveyed in shorter, simpler sentences. By taking the time to carefully analyze and parse long sentences, researchers and developers can gain a deeper understanding of complex ideas and unlock new insights that can drive innovation and progress.
Therefore, while it may be tempting to avoid long sentences altogether, it is important to recognize their value and invest the necessary time and resources to work with them effectively.
Errors in Earlier Stages
One important aspect to consider when it comes to Dependency Parsing is its reliance on earlier stages of NLP such as tokenization and POS tagging. It is crucial to ensure that these earlier stages are accurate since any errors in them can potentially lead to significant errors in the parsing stage.
In fact, one might argue that the accuracy of the entire NLP process is only as good as its weakest link, emphasizing the importance of paying close attention to every stage of the process. Without accurate tokenization and POS tagging, the parsing stage can be rendered ineffective, which highlights the need for proper quality control throughout the entire NLP pipeline.
Despite these challenges, modern dependency parsers like the one in SpaCy achieve high accuracy and are fast enough for many practical applications.
6.3.6 Advanced Techniques
In addition to the basic techniques for dependency parsing that were discussed earlier, there are more advanced techniques available. These techniques utilize machine learning algorithms to predict the relationships between words in a sentence.
One such technique is graph-based parsing. This method constructs a complete graph of all possible dependencies between words in a sentence. Once the graph is built, an algorithm is used to determine the most probable tree structure within the graph.
Another advanced technique is transition-based parsing. This method builds the dependency tree in a step-by-step manner by predicting one dependency at a time.
Although these techniques are not covered in this introduction, they are important areas of research in the field of NLP and can be explored further if you wish to deepen your understanding of this subject.
6.3 Dependency Parsing
Dependency parsing is a crucial task in Natural Language Processing that involves the analysis of the grammatical structure of a sentence, with particular attention to the dependencies between the words. This task is often used in many NLP applications, including machine translation, sentiment analysis, question answering systems, and information extraction.
Dependancy parsing is performed by using a parsing algorithm to analyze a sentence and identify the relationships between its words. The algorithm takes into account the parts of speech of each word, as well as the structure of the sentence, in order to generate a dependency tree that represents the relationships between the words. This tree can then be used to extract information from the sentence, such as the subject, object, and verb.
There are many different parsing algorithms available, each with its own strengths and weaknesses. Some of the most commonly used algorithms include the Stanford Parser, the Berkeley Parser, and the MaltParser. Each of these algorithms has its own unique approach to dependency parsing, and researchers continue to develop new algorithms as well.
While dependency parsing is a complex task, it is a critical component of many NLP applications. By understanding the relationships between words in a sentence, we can extract important information and insights from text data. In the next section, we will delve further into the details of dependency parsing and explore how it is used in practice.
6.3.1 What is Dependency Parsing?
Dependency Parsing is an important process in Natural Language Processing (NLP), which involves the analysis of a sentence in order to represent its grammatical structure. This analysis aims to define the grammatical relationships between words in a sentence and represents these relationships in the form of a directed graph.
The graph is constructed in such a way that nodes represent individual words and edges represent the grammatical relationships between the words. By analyzing the grammatical structure of a sentence, we can gain insights into the meaning and intent behind the text. These insights can be useful in a wide range of applications, from language translation to sentiment analysis and beyond.
For instance, consider the sentence: "The cat is sitting on the mat." In this sentence, "sitting" is the main (or 'head') verb. "The cat" is the subject of this verb, and "on the mat" is where the cat is sitting. So, the relationships would be something like this:
- "cat" -> "sitting" (the cat is doing the sitting)
- "on the mat" -> "sitting" (sitting where? on the mat)
Each of these relationships is a dependency, and together they form a dependency parse of the sentence.
6.3.2 Why is Dependency Parsing Important?
Understanding the grammatical structure of a sentence is crucial for comprehending its meaning. For example, consider the following sentences: "Alice saw Bob" and "Bob saw Alice". Both sentences contain the same words, but their meanings differ due to their grammatical structures. Dependency Parsing is a technique that helps us understand the structure of sentences, allowing us to comprehend their meanings.
Dependency Parsing is critical in machine translation applications, where the correct translation of a sentence depends on its grammatical structure. For example, in languages such as German and Hindi, the verb often appears at the end of a sentence. Therefore, we must understand the entire sentence's grammatical structure before we can begin translating. This is why Dependency Parsing is essential in such applications.
Dependency Parsing can be used in natural language processing tasks such as sentiment analysis and named entity recognition, where understanding the structure of a sentence can aid in identifying the sentiment or named entities.
6.3.3 Dependency Parsing with SpaCy
SpaCy library has built-in support for dependency parsing. Here's a simple example:
import spacy
# Load English tokenizer, tagger, parser, NER and word vectors
nlp = spacy.load('en_core_web_sm')
# Process whole documents
text = ("The cat is sitting on the mat.")
doc = nlp(text)
# Analyze syntax
for token in doc:
print(f"{token.text} <--{token.dep_}-- {token.head.text}")
This code will output:
The <--det-- cat
cat <--nsubj-- sitting
is <--aux-- sitting
sitting <--ROOT-- sitting
on <--prep-- sitting
the <--det-- mat
mat <--pobj-- on
. <--punct-- sitting
In this output, ROOT
denotes the main verb or action in the sentence. The abbreviations like det
, nsubj
, aux
, etc., represent the type of dependency relation.
6.3.4 Visualizing Dependencies
SpaCy also provides a way to visualize these dependencies using displaCy:
from spacy import displacy
# Visualize the dependency tree
displacy.serve(doc, style='dep')
This will start a web server and display a webpage with the visualized dependency tree.
6.3.5 Practical Considerations
Dependency Parsing is a complex task and has its challenges:
Ambiguity:
Some sentences can have multiple valid dependency parses. For instance, "I saw the man with the telescope." Could mean that I used a telescope to see the man (I -> saw -> man -> with -> telescope), or it could mean that I saw a man who had a telescope (I -> saw -> (man with telescope)). Determining the correct parse requires understanding the context and sometimes even world knowledge.
Long Sentences
Long sentences can be challenging to work with, especially when they contain complex grammatical structures that require sophisticated dependency parsing algorithms. These algorithms can be computationally intensive, which can cause processing delays and slow down the overall performance of a system.
However, despite these challenges, long sentences can also be incredibly rewarding to work with, as they often contain rich and nuanced ideas that cannot be conveyed in shorter, simpler sentences. By taking the time to carefully analyze and parse long sentences, researchers and developers can gain a deeper understanding of complex ideas and unlock new insights that can drive innovation and progress.
Therefore, while it may be tempting to avoid long sentences altogether, it is important to recognize their value and invest the necessary time and resources to work with them effectively.
Errors in Earlier Stages
One important aspect to consider when it comes to Dependency Parsing is its reliance on earlier stages of NLP such as tokenization and POS tagging. It is crucial to ensure that these earlier stages are accurate since any errors in them can potentially lead to significant errors in the parsing stage.
In fact, one might argue that the accuracy of the entire NLP process is only as good as its weakest link, emphasizing the importance of paying close attention to every stage of the process. Without accurate tokenization and POS tagging, the parsing stage can be rendered ineffective, which highlights the need for proper quality control throughout the entire NLP pipeline.
Despite these challenges, modern dependency parsers like the one in SpaCy achieve high accuracy and are fast enough for many practical applications.
6.3.6 Advanced Techniques
In addition to the basic techniques for dependency parsing that were discussed earlier, there are more advanced techniques available. These techniques utilize machine learning algorithms to predict the relationships between words in a sentence.
One such technique is graph-based parsing. This method constructs a complete graph of all possible dependencies between words in a sentence. Once the graph is built, an algorithm is used to determine the most probable tree structure within the graph.
Another advanced technique is transition-based parsing. This method builds the dependency tree in a step-by-step manner by predicting one dependency at a time.
Although these techniques are not covered in this introduction, they are important areas of research in the field of NLP and can be explored further if you wish to deepen your understanding of this subject.
6.3 Dependency Parsing
Dependency parsing is a crucial task in Natural Language Processing that involves the analysis of the grammatical structure of a sentence, with particular attention to the dependencies between the words. This task is often used in many NLP applications, including machine translation, sentiment analysis, question answering systems, and information extraction.
Dependancy parsing is performed by using a parsing algorithm to analyze a sentence and identify the relationships between its words. The algorithm takes into account the parts of speech of each word, as well as the structure of the sentence, in order to generate a dependency tree that represents the relationships between the words. This tree can then be used to extract information from the sentence, such as the subject, object, and verb.
There are many different parsing algorithms available, each with its own strengths and weaknesses. Some of the most commonly used algorithms include the Stanford Parser, the Berkeley Parser, and the MaltParser. Each of these algorithms has its own unique approach to dependency parsing, and researchers continue to develop new algorithms as well.
While dependency parsing is a complex task, it is a critical component of many NLP applications. By understanding the relationships between words in a sentence, we can extract important information and insights from text data. In the next section, we will delve further into the details of dependency parsing and explore how it is used in practice.
6.3.1 What is Dependency Parsing?
Dependency Parsing is an important process in Natural Language Processing (NLP), which involves the analysis of a sentence in order to represent its grammatical structure. This analysis aims to define the grammatical relationships between words in a sentence and represents these relationships in the form of a directed graph.
The graph is constructed in such a way that nodes represent individual words and edges represent the grammatical relationships between the words. By analyzing the grammatical structure of a sentence, we can gain insights into the meaning and intent behind the text. These insights can be useful in a wide range of applications, from language translation to sentiment analysis and beyond.
For instance, consider the sentence: "The cat is sitting on the mat." In this sentence, "sitting" is the main (or 'head') verb. "The cat" is the subject of this verb, and "on the mat" is where the cat is sitting. So, the relationships would be something like this:
- "cat" -> "sitting" (the cat is doing the sitting)
- "on the mat" -> "sitting" (sitting where? on the mat)
Each of these relationships is a dependency, and together they form a dependency parse of the sentence.
6.3.2 Why is Dependency Parsing Important?
Understanding the grammatical structure of a sentence is crucial for comprehending its meaning. For example, consider the following sentences: "Alice saw Bob" and "Bob saw Alice". Both sentences contain the same words, but their meanings differ due to their grammatical structures. Dependency Parsing is a technique that helps us understand the structure of sentences, allowing us to comprehend their meanings.
Dependency Parsing is critical in machine translation applications, where the correct translation of a sentence depends on its grammatical structure. For example, in languages such as German and Hindi, the verb often appears at the end of a sentence. Therefore, we must understand the entire sentence's grammatical structure before we can begin translating. This is why Dependency Parsing is essential in such applications.
Dependency Parsing can be used in natural language processing tasks such as sentiment analysis and named entity recognition, where understanding the structure of a sentence can aid in identifying the sentiment or named entities.
6.3.3 Dependency Parsing with SpaCy
SpaCy library has built-in support for dependency parsing. Here's a simple example:
import spacy
# Load English tokenizer, tagger, parser, NER and word vectors
nlp = spacy.load('en_core_web_sm')
# Process whole documents
text = ("The cat is sitting on the mat.")
doc = nlp(text)
# Analyze syntax
for token in doc:
print(f"{token.text} <--{token.dep_}-- {token.head.text}")
This code will output:
The <--det-- cat
cat <--nsubj-- sitting
is <--aux-- sitting
sitting <--ROOT-- sitting
on <--prep-- sitting
the <--det-- mat
mat <--pobj-- on
. <--punct-- sitting
In this output, ROOT
denotes the main verb or action in the sentence. The abbreviations like det
, nsubj
, aux
, etc., represent the type of dependency relation.
6.3.4 Visualizing Dependencies
SpaCy also provides a way to visualize these dependencies using displaCy:
from spacy import displacy
# Visualize the dependency tree
displacy.serve(doc, style='dep')
This will start a web server and display a webpage with the visualized dependency tree.
6.3.5 Practical Considerations
Dependency Parsing is a complex task and has its challenges:
Ambiguity:
Some sentences can have multiple valid dependency parses. For instance, "I saw the man with the telescope." Could mean that I used a telescope to see the man (I -> saw -> man -> with -> telescope), or it could mean that I saw a man who had a telescope (I -> saw -> (man with telescope)). Determining the correct parse requires understanding the context and sometimes even world knowledge.
Long Sentences
Long sentences can be challenging to work with, especially when they contain complex grammatical structures that require sophisticated dependency parsing algorithms. These algorithms can be computationally intensive, which can cause processing delays and slow down the overall performance of a system.
However, despite these challenges, long sentences can also be incredibly rewarding to work with, as they often contain rich and nuanced ideas that cannot be conveyed in shorter, simpler sentences. By taking the time to carefully analyze and parse long sentences, researchers and developers can gain a deeper understanding of complex ideas and unlock new insights that can drive innovation and progress.
Therefore, while it may be tempting to avoid long sentences altogether, it is important to recognize their value and invest the necessary time and resources to work with them effectively.
Errors in Earlier Stages
One important aspect to consider when it comes to Dependency Parsing is its reliance on earlier stages of NLP such as tokenization and POS tagging. It is crucial to ensure that these earlier stages are accurate since any errors in them can potentially lead to significant errors in the parsing stage.
In fact, one might argue that the accuracy of the entire NLP process is only as good as its weakest link, emphasizing the importance of paying close attention to every stage of the process. Without accurate tokenization and POS tagging, the parsing stage can be rendered ineffective, which highlights the need for proper quality control throughout the entire NLP pipeline.
Despite these challenges, modern dependency parsers like the one in SpaCy achieve high accuracy and are fast enough for many practical applications.
6.3.6 Advanced Techniques
In addition to the basic techniques for dependency parsing that were discussed earlier, there are more advanced techniques available. These techniques utilize machine learning algorithms to predict the relationships between words in a sentence.
One such technique is graph-based parsing. This method constructs a complete graph of all possible dependencies between words in a sentence. Once the graph is built, an algorithm is used to determine the most probable tree structure within the graph.
Another advanced technique is transition-based parsing. This method builds the dependency tree in a step-by-step manner by predicting one dependency at a time.
Although these techniques are not covered in this introduction, they are important areas of research in the field of NLP and can be explored further if you wish to deepen your understanding of this subject.