A Complete Guide Of Natural Language Processing (NLP)

Due to the introduction of Artificial Intelligence and its wide spectrum use cases, we have heard about Machine Learning as well as Natural Language processing. What is natural language processing? It is a multidisciplinary field that is large and comprises many sub-areas. It comes under AI and ML and is used by millions of businesses. For instance, LLM SEO optimization assists with LLM’s content optimization used by businesses to improve their online visibility. In this blog, you will get to know what is natural language processing is and a complete NLP guide. 

What is Natural language processing?

Natural language processing is all about making computers learn, understand, analyze, manipulate and interpret natural human language. NLP stands for Natural Language Processing which is a part of computer science, Linguistics and Artificial Intelligence. It is essential to be required if you want to make an intelligent system such as machine learning, AI and robots. The ability of machines to interpret human language is now at the core of many applications that we use every day such as chatbots, grammar checkers and spelling, Optical Character Recognition, augmentative and alternative communication, machine-aided translations, spam filters, screen readers for blind and partially sighted users, summarization search engines, and voice assistants. For your website, you need help from the Best Web Design Company in Dubai.

NLP process and approach

The process and deployed approach towards a natural language processing system varies due to many factors. The following are a few relevant NLP approaches used by scientists, computer engineers and AI developers.

1. Symbolic – Symbolic approach weighs rules created by humans used to create this kind of language model.  It is extensively used for the creation of chatbots as well as sentiment analysis.

2. Connectionist – In this approach, what they do is look for a database of similar patterns to derive conclusions. Prediction of data, speech and image recognition are some of the applications.

3. Rule-based – Text or human language has linguistic rules anyway. This NPL approach makes use of linguistic rules of various kinds to form conclusions from a set of textual data. Computation performance, data annotation, e-commerce chatbots and language modelling are the use cases of the rule-based approach to NPL.

4. Supervised – Here, specific and accurate voluminous textual data as labels are used to train a tool or software to provide the correct outcome. Recommendations and sentiment analysis are the two important applications of the supervised approach.

5. Unsupervised – Statistical language model is the main input of the unsupervised NLP approach and it is widely used in data classification based on content and recognition of voice as well as a set of data.

General NLP Steps

Steps in NLP There are general five steps : 

• Lexical Analysis  – Identifying named entities, tokenization and assigning grammatical categories are part of this.
• Syntactic Analysis (Parsing) – It is used to check grammar and word arrangements, and shows the relationship among the words.
• Semantic Analysis – It mainly focuses on the literal meaning of words, phrases, and sentences.
• Discourse Integration – Here, the context is given huge importance within a large textual data
• Pragmatic Analysis –  It is all about interpreting natural language or the content in the real world. It reinterprets what has been articulated.

NLP examples

• Search Autocorrect

1. Autocorrection or auto-filling of words when someone types two to three words
2. It makes easy to locate accurate results

• Translation

1. Software or tools used to convert ideas from one language to another.
2. It can be a mechanical translation that is translating word to word or a semantic translation
3. Corpus statistical and neural techniques used for translation.

• Informational retrieval

1. Return of set of documents for a user query
2. Search engines used information retrieval NLP models
3. It garners specific information from a set of data

• Text classification such as filtering and routing

1. Unstructured text needs to be organised using text segmentation or classification
2. Here, pre-defined tags are used after the automatic classification of data
3. It helps in automating business

• Email Filtering

1. The fundamental and most relevant application of NPL is email filtering
2. Identify specific words and phrases indicating spam message
3. Several algorithms are used to determine categories.

• Text extraction

1. Extracting information from unstructured textual data
2. Names, locations, domains and quantities can be interpreted
3. Text extraction benefits the healthcare, finance and e-commerce sectors

• Dialogue systems

1. Human interaction with a computer using gestures and speech
2. Spoken dialogue systems use natural language interfaces

• Sentiment Analysis

1. Here, the emotional tone of a text can be easily deciphered.
2. Companies use it to understand what customers ponder about their brand as a whole or services/products.
3. It helps businesses to get real-time sentiment data to be used for marketing strategies.
4. Positive, negative or neutral feedback.

• Speech Recognition

1. Provides methodologies and technologies that enable speech recognition
2. Automotive, healthcare, technology and sales are some of the important sectors that use this.

Benefits of NLP examples

• Repetitive tasks that need automation – Partially automated tasks can be executed using NLP systems in domains such as typing jobs,  document analysis, chatbots and customer management.
• Data analytics and apprehension – Spelling and grammar check, OMR sheet evaluation, and retrieving data from various documents such as articles, PDFs, blogs and social media can be easily carried out to make the best decisions.
• Automated content generation – Churning out numerous types of content on diverse subjects is a piece of cake with a meticulous NLP guide. The content produced by the NLP learning model is similar to human-produced content in many ways. Depending on the accuracy of the data, you can produce reliable content. Startups, digital marketing companies and big businesses need content for marketing and this need can be satisfied to an extent with the NLP guide.
• Enhanced search – Google wants businesses or publishers to consider the search intent of any user to answer their question. It is the NLP process that makes the best prototypes to elevate the search experience.

Challenges in NLP

Though we can see a lot of NLP tutorials online, there are challenges involved when it comes to the adoption of natural language processing. 

• Development time and resources
• Datasets as labels are prone to errors compromising results
• Misspellings and linguistic errors can be voluminous
• It is full of biases
• In several languages, words have different meanings and connotations