In this article, we will talk about natural language processing. We will define what it is, where we use programming languages, what is their contribution, the most popular applications of it, and their usage in different fields. In the end, we will discuss if NLP could ever replace human support.
What Is NLP?
Natural Language Processing (NLP) is a subfield of Computer Science, Information Engineering and Artificial Intelligence concerned with the interactions between computers and human language. It focuses on processing and analyzing large amounts of natural language data. Also, it focuses on how to get computers closer to a human-level understanding of language.
The main aim is to make the computer as intelligent as a human being in understanding the language. The developer can perform a task such as sentiment analysis, speech recognition, and relationship extraction. Challenges in natural language processing involve speech recognition, natural language understanding, and natural language generation.
The Technical Side of NLP
Certain programming languages help to program NLP.
Some of them are R, Python, and Java.
Programming language R is used for statistical learning.
It understands and explores your data using statistical methods and graphs. R plays an important role in investigating big data, supporting the researcher, and also it is useful for intense learning analytics. It has an enormous number of natural language processing algorithms.
Another one is Python, a high-level object-oriented scientific programming language.
Python is easy to learn syntax readability and reduce the cost of maintenance. It contains a lot of packages using this you can do code reusability.
Also, it can extract information from unstructured text, either to guess the topics and identity named entity. Using Python, parsing and semantic you can analyze language structure.
The third one and the most popular programming language for Android Smartphone is Java.
Java helps you to organize text using full-text search, clustering, tagging, and information extraction. It is a platform independent language because of this feature the processing of information becomes easy.
Some of the applications of this new technique are as an enhancement for grammar checking software such as Grammarly or a writing platform like Twinword Writer.
Another application is used to translate from one human language to another. Translating with computer help would cut down the time needed for translating documents.
More demanding applications of NLP are chatbots and humanoid robots. Chatbots or intelligent agents answer all types of questions and inquiries from their users. Humanoid robots, such as gynoids, use NLP as their base of understanding and responding to different stimulants.
NLP in Social Media
The development of social media has revolutionized the amount and types of information available today to NLP researchers.
Data available from social media such as Twitter, Facebook, YouTube, blogs, and discussion forums make it possible to find relations between demographic information, language use, and social interaction.
Using statistical and ML techniques, researchers can learn to identify demographic information, language, track trending topics, and predict disease spreading.
For instance, with Google Flu Trends it recognizes deception in fake reviews from symptoms mentioned in a tweet or food-related illnesses.
Social media has changed the ways we can use big data. Product reviews can help to predict pricing trends and plan future advertising campaigns. Political forums help to predict success in elections.
Equally important, social networks can be used to determine indicators of influence among different groups. Similarly, medical forums can contribute to the discovery of questions about patients suffering from a particular medical condition.
Focus on Sentiment Analysis
What is more important, much of the work in NLP has focused on sentiment analysis. The sentimental analysis determines opinions, committed or uncommitted beliefs, emotions, positive or negative orientation. In the same way, it determines the connotation of textual language or speaking language.
All mentioned is based on lexical and syntactic information. Positive and negative orientation signal by words sentimental attitudes.
For example, words with a negative connotation are “sad,” “worried,” “difficult,” and “weak”. In contrast, “comfortable,” “important,” “successful,” and “interesting” convey a positive sentiment.
Online sentiment dictionaries, such as Whissel’s Dictionary of Affect, can assess positive and negative sentiment in text. However, there are new practices for identifying emotions based on six basic ones.
There has also been researching using features that can recognize classic emotions to identify speaker medical conditions.
For instance, it can recognize autism and Parkinson’s disease. In addition to medical conditions, it can recognize speaker characteristics such as age, gender, likeability, pathology, and personality.
Moreover, it can recognize the speaker’s conditions like cognitive load, drunkenness, sleepiness, interest, and trust.
What Is Next for NLP?
Recent advances in Machine Learning (ML) together with deep learning have enabled computers to do quite a lot of useful things with NP.
Furthermore, it has enabled us to write programs to perform things like language translation, semantic understanding, and recognizing emotions. Although there is a disadvantage where computers don’t yet have the same intuitive understanding of natural language that humans do.
Because of that, it’s impossible for them to “read between the lines”. That’s why it’s justifiable to doubt that they won’t be able to do a better job than humans.
But, if we reconsider that humans also often are not able to “read between the lines”, can handle less information and are slower — NLP is a great opportunity to take over some humans responsibilities.
Originally published at www.paldesk.com on February 19, 2019.
