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In the world of natural language processing (NLP), parsing plays a vital role in understanding and analyzing human language. In this article, we will dive deep into the concept of parsing, exploring its definition, types, and applications. We will also provide real-time examples to help you grasp the concept better.
What is Parsing?
Parsing is the process of analyzing a sentence, breaking it down into smaller components, and identifying the grammatical structure of the sentence. It is a crucial component of NLP and helps machines understand human language. In other words, parsing is the process of analyzing a sentence’s syntax and its underlying structure to extract meaning from it.
Example of Parsing in action
Let’s try to understand parsing by an example sentence. The sentence “Stephen is playing guitar”. Once we parse this sentence, it will be stated into individual constituents as “Stephen”, “is”, “playing”, and “guitar”.
In the above sentence, parsing works by first breaking it down to individual tokens i.e., “Stephen”, “is”, “playing”, and “guitar” which are nothing but individual words making up the sentence.
In the next steps, part of speech is tagged like a Noun tagged to “Stephen” and “guitar” whereas Verb is tagged to “is” and “playing”.
As per the above example, it is evident that parsing a natural language sentence involves analyzing the input sentence by breaking it down into its grammatical constituents, identifying the parts of speech, and syntactic relations.
What is Shallow Parsing and Why it’s not sufficient?
Shallow parsing is fairly limited (shallow) levels of parsing such as POS tagging, chunking, etc. We will learn chunking in the subsequent lessons.
Shallow parsing is a limited form of parsing that cannot check the grammatical structure of the sentence, i.e. whether a sentence is grammatically correct, or understand the dependencies between words in a sentence.
That’s the reason Shallow parsing is used in less complex NLP applications.
Types of Parsing
There are mainly four types of parsing which are as follows:
- Top-down parsing
- Bottom-up parsing
- Constituency parsing
- Dependency parsing.
In the subsequent lessons, we will learn different types of parsing. You can also visit any of the parsing techniques by clicking on hyperlinks.
Applications of Parsing in NLP
Parsing has several applications in NLP. Some of the common applications are:
Parsing is useful in machine translation, where it helps machines understand the structure of a sentence and the relationships between the words. Parsing is used to identify the parts of speech of the words in a sentence and their relationships with other words. This information is then used to translate the sentence into another language.
Parsing is useful in sentiment analysis, where it helps machines understand the sentiment of a sentence. Parsing is used to identify the subject and object of a sentence and their relationships with other words. This information is then used to determine the sentiment of the sentence.
Parsing is useful in question answering, where it helps machines understand the structure of a question and identify the answer. Parsing is used to identify the parts of speech of the words in a question and their relationships with other words. This information is then used to identify the answer.
Proceed to Lesson Constituency Parsing