Natural language processing (NLP) is a subfield of artificial intelligence. It is the ability to analyze and process a natural language. Natural language is the representation of meaning in human communication.
This field covers research to understand the structure and meaning of language.
In NLP, you can find some of the interesting research topics such as:
- How to understand the meaning of text?
- How to take the meaning of a sentence and extract it from the context?How to understand the meaning of the word?
- How to analyze the sentence structure?
- How to understand the sentence structure to create the meaning of the sentence?
Some of the most widely used tools to do NLP are:
- NLTK
- Gensim
- Stanford NLP
- SpaCy
- OpenNLP
How Does Natural Language Processing Work?
A full explanation of how NLP works is beyond the scope of this article. In this section, I simply provide a high-level overview of NLP.
NLP consists of two main tasks: understanding and generation.
Understanding is the task of taking in a sentence and making sense of it. Generation is the task of taking in a meaning and using it to generate a sentence.
Understanding
The first step in understanding a sentence is to identify its parts of speech (or word types). The next step is to use the part-of-speech information to identify the syntactic relationships between the words.
In English, the syntactic relationships are similar to those in a sentence diagramming exercise in school.
Consider the following sentence: The dog chased the cat.
To analyze this sentence, we first identify the parts of speech of each word:
To analyze this sentence, we first identify the parts of speech of each word:
The: noun
dog: noun
chased: verb
the: article
cat: noun
We then identify the syntactic relationships between the words.
The dog is the subject of the sentence. The cat is the direct object of the sentence. The word the is an article.
dog: noun
chased: verb
the: article
cat: noun
We then identify the syntactic relationships between the words.
The dog is the subject of the sentence. The cat is the direct object of the sentence. The word the is an article.
The next step is to identify the meaning of the sentence. We can do this by using a language model, which essentially maps parts of speech to meanings. In the case of verbs, the meaning is a verb’s action.
For example, the verb chased means “to follow with the intent to catch.” The final step is to make inferences from the sentence to fill in any missing information. For example, we can infer that the cat was not caught.
Generation
The task of generation is to use a meaning to generate a sentence. Consider the following sentence: The dog chased the cat.
The first step is to identify what the sentence means. The next step is to use this meaning to generate a sentence.
To do this, we can take the meaning and create a new sentence by concatenating words together.
For example, we can concatenate the words dog and chased to create a sentence “The dog chased.”
We could then concatenate the words dog and cat to create a sentence “The dog chased the cat.”
This approach is called “sequential generation,” and it is a key element of generative systems.
What is NLP Used for?
Natural Language Processing is used to understand the meaning of texts. For example, it can be used to automatically translate text between languages.
It can also be used to extract information from documents for processing or to act upon. It can also be used to recognize the sentiment of a piece of text.
NLP Use Cases
We selected a few of the NLP use cases to show the different uses for the NLP APIs. We plan to write a series of blog posts to explain a few of these use cases.
Natural Language Search
Natural language search is probably the most commonly used NLP use case.
The NLP search APIs are used to search documents for relevant text. The search results are returned as a list of documents ranked by a score. The higher the score, the more relevant the document is to the user’s search query.
For example, suppose you are building a search engine for a large collection of documents. You can use the NLP search APIs to find all the documents that contain the term “JavaScript.” The NLP search APIs allow you to return the results as a list of documents, each with a score that indicates how relevant it is to the search query.
Language Detection
Language detection is another common NLP use case. NLP language detection APIs allow you to detect the language of a document.
For example, suppose you have a collection of documents that you would like to make available in multiple languages. You can use the NLP language detection APIs to determine the language of each document and make them available to users in the language they prefer.
Entity Analysis
Entity analysis is another common NLP use case. The NLP entity analysis APIs allow you to extract entities, such as people, locations, organizations, and products, from a document.
The extracted entities are returned as a list of dictionaries.
For example, suppose you are building a travel website. You can use the NLP entity analysis APIs to extract all the relevant information about a place from a document.
You can use the extracted entities to display information about the place in a user-friendly way.
Text Classification
Text classification is another popular NLP use case. The NLP text classification APIs allow you to classify text into one of several categories. You can use the NLP text classification APIs to classify text into a category that has a known taxonomy, or you can create your own taxonomy.
For example, suppose you are building an email application. You can use the NLP text classification APIs to classify incoming emails into one of several categories. You can use the categories to display the appropriate messages to the user.
Final Thoughts
There is still so much to talk about when it comes to Natural Language Processing, but today I just wanted to give a brief overview of what it is, how it works, and what it's used for.
Stay tuned for more articles on NLP and Artificial Intelligence!