Are you looking for a source that will provide you with all the information you require regarding natural language processing? If so, let me inform you that I will begin an NLP blog series today, with this post serving as the introduction to the series. So without further ado, let's get going.
What is NLP?
The term "NLP" stands for "natural language processing," and it refers to a branch of linguistics, computer science, and artificial intelligence that focuses on developing a human-computer interface so that humans could easily communicate with machines using natural language instead of traditional programming.
Real-world applications of NLP
Natural language processing has several effective real-world applications, which is why many businesses are investing heavily in the continued development of this technology. Real-world examples of applications include:
Chatbots and conversational systems: NLP algorithms can be used to develop chatbots and other conversational systems that can understand and respond to user input in natural language, enabling customer service automation and improving efficiency.
Text classification: NLP algorithms can be used to classify text into predefined categories, such as spam or non-spam email and relevant or irrelevant documents etc.
Text summarization: NLP algorithms can be used to automatically generate summaries of long documents or articles, which can save time and improve the efficiency of information consumption. Example: Inshorts
Sentiment analysis: NLP algorithms can be used to analyze text or speech to determine the sentiment or emotion expressed, which can be used to track customer satisfaction or public opinion.
Approaches used in NLP
Natural language processing primarily employs three different methodologies, each of which has its own advantages. These strategies include:
Heuristics-based approach: In the heuristics-based approach some rules are predefined by humans to analyze the natural language data. Example: heuristic-based technique can be used for the sentiment analysis, where first of all the total number of positive, neutral, and negative words are counted, and the label with the highest count is used to categorize whether a text is positive, negative, or neutral. Another application is a parts-of-speech (POS) tagger, in which a rule can be established that the word "the" should be tagged as a determiner and the word "ran" as a verb.
Machine learning-based approach: In a machine learning-based approach for NLP, machine learning algorithms such as ( naive Bayes, logistics regression, SVM, LDA and hidden markov model ) are trained on a large dataset of natural language data.
Deep learning-based approach: Deep learning-based approach in NLP involves the use of artificial neural networks to process and analyze natural language data. These networks are composed of layers of interconnected nodes and are trained to recognize patterns and relationships in the data. Some of the neural network architectures that are used for NLP are RNN, LSTM, GRU-RNN, transformers and auto-encoders.
Challenges in NLP
NLP is one of the most challenging technologies to work on because of the many problems we must overcome while creating any NLP-based application. Some of these challenges include:
Ambiguity in a sentence: Natural language is ambiguous which makes it difficult for machines to understand and interpret. For example, I saw a boy on the beach with my binoculars. This sentence can have 2 different meanings and machines can't understand what humans mean.
Use of slang: Slang words and phrases often have meanings that are different from their formal or dictionary definitions. For example, This work is piece of cake for me is slang, which is difficult for a machine to understand because by using this slang we are trying to convey that this work is pretty easy for me.
Contextual words: Contextual words are words whose meanings depend on the context in which they are used, and they can be difficult for NLP systems to understand and interpret. For example, I ran to the store because we ran out of milk, now in this sentence, the word "ran" is used 2 times but with a different contexts.
Short note
I hope you good understanding of what is natural language processing, applications of it and also the challenges faced. So if you liked this blog or have any suggestions kindly like this blog or leave a comment below it would mean a to me.