“In recent years, NLP has witnessed several breakthroughs in helping computers understand human language,” according to Leand Romaf, a software engineer and AI expert.
Natural Language Processing, one of the most advanced technologies that help computers understand human language. However, the process of teaching machines to understand our way of communication is quite technical and challenging.
Nevertheless, those in the field of artificial intelligence are striving to make the process less cumbersome and more efficient to improve common applications related to voice searches and translations.
The objective of this article is to introduce the Natural Learning Process, its uses, and ways to excel it.
What is Natural Language Processing (NLP)?
A sub-field of Artificial Intelligence, Natural Language Processing, focuses on the interaction between computers and humans. For this, software engineers devise a common language known as a natural language to improve communication between machines and homo sapiens.
Through NLP, machines, more specifically, computers can read, interpret, understand, and comprehend human language the way we do for more valuable results. The processing, typically, is based on the machine’s intelligence level of decoding human messages into meaningful communication.
A standard NLP requires the following steps to progress the interaction between humans and machines. Following is a 7-step process:
- You talk to the machine
- It records the audio
- Converts audio to text
- Deciphers the text data
- Responds to the data
- Converts the results into audio
- Plays the audio data to respond to human interaction
Why do we need Natural Language Processing?
It is one of the most powerful tools to operate various yet very common machine applications, such as online translators and other voice-based response. Typically, these include:
- Language translation tools, including Google Translate
- MS word, Grammarly and other language tools used for checking grammar accuracy
- Auto-generated voice message tools that are mainly used in call centers and customer care departments
- Mobile or web-based assistant tools such as Siri, OK Google, and Alexa.
What Makes NLP So Tough?
NLP is considered one of the most defiant technologies of computer sciences due to the complex nature of human communication. It is not easy for machines to comprehend the context of dictated information.
It could be quite an abstract milieu, which changes the meaning and understanding of the command. The most common example is sarcastic remarks used to pass information.
Other than that, plurals with an “s” also create confusion sometimes; thus, the machine needs to decipher the words as well as the contextual meaning to comprehend the entire message.
Due to our high-level intelligence, humans can easily master a language as we first try to understand the situation the phrase is used. But natural languages are based on ambiguous and imprecise characteristics, making it difficult for machines to use NLP.
Algorithm —The Backbone of Natural Language Processing
Natural Language Processing is based on algorithms to translate ambiguous data into comprehensive information for the machines to build understanding. These algorithms use various natural language rules to perform the task.
When the information is provided to the computer, it will use another set of algorithms to comprehend the contextual meaning associated with the command and then collect relevant data required for the query.
However, at times, the computer provides obscure results based on the fact that it failed to understand the contextual meaning of the command. For instance, posts on Facebook typically fail to translate correctly due to poor algorithms.
You will often read humorous (translated) posts on various Facebook groups, just because the natural language of Facebook is unable to connect the associated meaning of every word or sentence.
One of the most commonly quoted public incidents was in the 195os when some Russian Biblical message was translated in English.
The Russian message, “The spirit is willing, but the flesh is weak,” was translated into “the vodka is good, but the meat is rotten.” You can still find such hilarious translations as machines are not capable of fully mastering human interaction.
How Does NLP work?
Natural Language Processing uses two main techniques: syntactic analysis and semantic analysis to perform all the tasks. Let’s review each technique in detail to understand NLP performance:
- Syntax/Syntactic Analysis
It deals with the placement of words to ensure grammatical accuracy. This analytical algorithm arranges words for a cohesive sentence without any composition errors.
The technique assesses the alignment of natural language with the grammatical rules for flawless understanding. The algorithms extract a group of words and imply grammatical rules to derive their meaning.
Few common syntax techniques are:
- Lemmatization: it is a linguistic process that groups together modulated words that can be analyzed with a single term, characterized under a lemma (dictionary form).
- Morphological Segmentation: it breaks a group of words into meaningful phrases or morphemes
- Word Segmentation: it deals with dividing structured sentences into component words.
- Part-of-speech Tagging: the process identifies the parts-of-speech in each sentence to apply grammatical rules.
- Parsing: it deals with performing grammatical analysis on each sentence.
- Sentence Breaking: the process separates one sentence from the other, thus setting boundaries to a set of words.
- Stemming: it works on associating the inflected word with its root form, such as consulting, consultant, consultative, and consultants will be associated with the root word “consult.”
- Semantic Analysis: It refers to the process of focusing on the contextual meaning of words. Being the toughest part of Natural Learning Processing, the technique is still in its developmental stages.
The process uses algorithms to extract the meaning to decipher words and sentences according to the structures. The common methods that semantic analysis uses are:
- Named Entity Recognition (NER): the process deals with the identification and categorization of words into certain groups, such as names of people or places.
- Word Sense Disambiguation: it deals with adding contextual meaning to the word based on the sentence framework.
- Natural Language Generation: it uses a database to decipher logical meaning to word text and convert the collected information into human language.
Natural Language Processing is core to human-machine communication and uses various techniques to improve the tasks.
It is still in its evolving stage and therefore requires major breakthroughs to make machines smarter and achieve perfectionism in human interaction. Have you ever relied on any of the NLP techniques to improve the functionality of your applications?