What is the relationship between Natural Language Processing and Artificial Intelligence? Learn about some of the use cases in this article.
Broadly speaking, AI or Artificial Intelligence is a branch of computing that’s dedicated to designing and implementing systems capable of ingesting information, drawing inferences from data, then performing actions in a manner similar or superior to the way that human beings react. Natural Language Processing or NLP in AI is the sub-branch of artificial intelligence whose focus uses natural language as the medium of interaction between humans and machines.
To make the results seem natural, processing language in AI requires a combination of computer science and linguistics. NLP developers must first understand the structure and rules governing language before constructing intelligent systems that can analyze, understand, then extract meaning from speech or written text.
With the internet contributing vast quantities of unstructured data in the form of text, Natural Language Processing enables systems to make sense of online news and reviews, social media commentary, customer communications across various industries, and a host of other text-based information streams.
NLP also allows for the design of industry-specific tools that harness the power of language interaction. Many can process information in real-time and correctly interpret casual and idiomatic usage of human language while providing responses that are virtually indistinguishable from natural speech or writing. A chatbot using natural language processing to give information or customer service is one example of this level of NLP technology.
NLP Mechanics and Methodology
Natural language processing tools can transform text into a format that machines can understand through a process known as text vectorization. The next phase is to feed training data into machine learning algorithms to train systems into making the correct association between a particular input stream and its corresponding output.
Once this initial training is complete, systems can then employ statistical analysis methods to construct their own knowledge bases, which enable them to determine the features most appropriate for representing or responding to particular text and make educated predictions for new or unseen text.
Using advanced deep learning algorithms, it’s possible to string together many natural language processing tasks in sequence and apply them all at once to perform complex analysis of text streams.
Natural Language Processing Techniques
Various techniques are employed in natural language processing, many with the ultimate aim of breaking human language down into portions of data that machines can read and interpret. They include:
Syntactic Analysis or Parsing
In this technique, NLP systems analyze the syntactic or phrase and sentence structure of a piece of text and the dependent relationships between its words. The system represents the results in a special kind of diagram known as a parse tree.
Semantic Analysis
The objective of semantic analysis is to establish the meaning of a piece of text. To break down the complex architecture of a language, semantic tasks look at sentence structure, interactions and relationships between words, and other related ideas. Ultimately, this analysis should enable a system to identify the meanings of words and understand the subject of a particular text passage.
Tokenization
A core element in Natural Language Processing, tokenization deconstructs a string of words into units known as tokens which have a particular semantic value to an NLP system. Sentence tokenization and word tokenization are employed for the sentences in a text passage and the words within its sentences, respectively.
As a general rule, blank spaces will separate different word tokens, while stops or periods come between each sentence token. When dealing with more complex word structures such as place names, systems may employ high-level tokenization to identify collocations or words that often appear together.
Part-of-Speech (PoS) Tagging
An essential NLP mechanism for establishing the relationships between words and the larger meaning of sentences, Part-of-Speech or PoS tagging assigns a part of speech tag or category (noun, pronoun, verb, etc.) to each token in a piece of text.
Dependency Parsing
Independency parsing, an NLP system, analyzes the various ways in which the words within a sentence are connected—identifying how each word is related or modified by other words assists in comprehending the syntactic structure of a sentence.
Lemmatization and Stemming
The root form of a word as it occurs in a dictionary is called a lemma. NLP systems use lemmatization to select the appropriate lemma for a word based on context. As an example, the lemma “be” includes “am, are, were, is, and been” under its root umbrella. So an NLP system using lemmatization would choose the most appropriate form in the context of a given sentence.
Another type of root form of a word is called a stem. This term is used in cases like “assist,” “assistant,” or “assisting.” In natural language processing, stemming operates on single words without considering their context. It trims words down to their root or stem form for simplicity and speed of machine comprehension and can lead to less accurate or semantically incorrect results.
Named Entity Recognition (NER)
A common feature in semantic analysis, named entity recognition, extracts entities such as names and email addresses from a piece of text.
Stopword Removal
This technique filters out high-frequency words such as “is,” “to,” “at,” or “for,” which may add little or no semantic value to a sentence. Some NLP systems allow you to set up lists of words that you can safely ignore.
Text Classification
In natural language processing, text classification is employed to help make sense of unstructured text by organizing it into pre-defined categories or tags.
Sentiment analysis is a popular form of text classification that aims to classify unstructured text streams based on the emotional response, opinion, or sentiment they contain. Language detection, intent detection, and topic modeling are other forms of text classification in NLP.
Word Sense Disambiguation
The same word may have a variety of meanings in different contexts. A knowledge-based or dictionary approach to word sense disambiguation tries to extract the meaning of doubtful words in a piece of text from their dictionary definition. A supervised approach to word sense disambiguation relies on NLP algorithms based on machine learning data.
NLP in Action
Email filtering is one of the most widely used forms of natural language processing. NLP systems for email filtering, such as those in Gmail, are responsible for sorting incoming messages into various logical categories (Promotion, Important, etc.) based on their analysis of subject lines and keywords in the message text.
Virtual or voice assistants like Amazon Alexa or Apple’s Siri also rely on NLP technology for their operation. Natural language processing algorithms allow these systems to interpret and respond to voice requests from the user automatically. They can also enable customized training for various individual users and learn from previous interactions and related queries.
For Frequently Asked Questions, customer service, and technical support across various industries, the natural language processing chatbot has become a familiar sight and experience for consumers.
Some of these systems rely on a rule-based approach to their deployment, drawing on a pre-defined and limited set of responses to speech or text-based input. This has the advantage of speed, but can be lacking to the user in terms of similarity to actual human dialogue.
However, if your deployment of chatbots relies on artificial intelligence and natural language processing, there’s scope for more genuinely conversational interaction with the user. Systems with intent recognition can quickly establish what the user is getting at within the space of a few words, and offer up a natural response. By using NLP to differentiate between the various types of requests coming from users, AI-powered chatbots are able to enhance the customer experience significantly.
NLP chatbots incorporating sentiment analysis are becoming increasingly sophisticated and capable of accurately identifying the emotions being displayed by the human they’re interacting with. Advanced algorithms also enable a chatbot with Natural Language Processing to interpret and understand slang, idiomatic phrases, and nuances across a range of different languages.
