NATURAL LANGUAGE PROCESSING

Natural Language Processing (NLP) can be defined as a machine’s capacity to comprehend natural language or human language as it is spoken and written. It is a part of Artificial Intelligence (AI). NLP blends statistical, machine learning, and deep learning models with computational linguistic i.e., rule-based modeling of human language. With the use of these technologies, computers can now process human language in the form of text or audio data and fully "understand" what is being said or written, including the speaker’s or writer's intentions and sentiments.

Chances are most people have already been using some of the most potent NLP applications but are not yet aware of that. Examples of the most commonly used NLP applications are Google Translate, Google Assistant, Amazon Alexa, Siri, etc.?

Why NLP is important?

• Structuring unstructured data

Human language is incredibly complex and varied. We exchange information innumerable times, both vocally and in writing. Not only are there different dialects and languages, but each language also has its own unique set of terminology, slang, and grammar standards. When we write, we frequently spell things improperly, abbreviate them, or omit punctuation. We speak with regional accents, mutter, fumble, and borrow words from other tongues. Although supervised and unsupervised learning, as well as explicitly deep learning, are currently frequently used to model human language, there is still a need for syntactic and semantic comprehension as well as domain knowledge, which are not present in these machine learning approaches. NLP is important because it helps resolve linguistic ambiguity and gives the data useful numeric structure for some downstream applications, such as speech recognition or text analytics.

• Handling huge volumes of text data

NLP has become more widely used thanks to big data technologies since it can now be used to process massive amounts of text data quickly and efficiently using cloud and distributed computing. Automation will be essential to effectively evaluate text and audio data given the astonishing volume of unstructured data produced daily, from social media to medical records.

NLP has altered how we communicate with computers and will do so going forward. As these AI technologies change and advance communication technology in the years to come, they will serve as the fundamental driver for the transition from data-driven to intelligence-driven initiatives.

How does NLP works?

In natural language processing, human language is broken into segments so the context is inferred from the relationships between these words, and the system is able to extract meaning. This enables computers to read and comprehend spoken or written text in a manner similar to that of people.

NLP mainly consists of two phases:

1. Data Preprocessing

Data preprocessing involves preparing and "cleaning" text data for machines to be able to analyze it. It consists of the following steps:

• Tokenization: Text is divided into manageable chunks at this point.
• Stop word removal: Common words are eliminated in this case, leaving only the special words that reveal the most about the text.
• Lemmatization and Stemming: Words are boiled down to their basic components at this point for processing.
• Part-of-speech tagging: Words are then labeled according to the part of speech they belong to, such as nouns, verbs, and adjectives.

2. NLP Algorithms

• Following the pre-processing of your data, you should create an NLP algorithm and train it to understand natural language and carry out certain tasks. ?Algorithms can be formed in two ways to drive the NLP training model.
• Rule-based approach: Algorithms are created by linguistic engineers and follow manually crafted grammatical rules
• Machine Learning Algorithms: Machine learning models, on the other hand, are based on statistical methods and learn to perform tasks after being fed examples (training data). As a result of this training, associations between words are recognized, which feeds into the machine knowledge bank to determine the text's purpose and gives businesses valuable data insights that improve commercial opportunities. Additionally, as training is increased, the knowledge bank grows, producing predictions that are more accurate and logical while producing fewer false positives.

Applications of NLP

There are so many real-world applications in today’s business, some of which are listed below:

1. Sentiment Analysis: Sentiment analysis can examine the language used in social media postings, comments, reviews, and more to extract attitudes and emotions in response to products, promotions, and events. Businesses can use this information to create new products, launch new marketing initiatives, and more.
2. Machine Translation: Machine translation is the process of automatically translating content from one language to another without any human input. Google Translator as mentioned above is an example of machine translation through NLP
3. Application in Healthcare: Natural language processing converts free text into standardized data, improving the completeness and quality of electronic health records. NLP aids in patient analysis and helps doctors understand the complexity of phenotyping. NLP algorithms assist healthcare organizations (HCO) in tracking potential problems in healthcare services. Identification of high-risk patients is facilitated by NLP predictive analysis, which enhances diagnosis procedures.
4. Virtual Agents and Chatbots: Virtual assistants like Apple's Siri and Amazon's Alexa recognize patterns in voice commands using speech recognition, and they answer with the necessary action or helpful comments using natural language generation. Chatbots respond to typed text entries by performing the same action. The greatest of these also pick up on contextual cues from human queries over time and employ them to offer even better selections or responses.
5. Advertisement Channel Applications:?Natural language processing is employed to implement keyword matching, which is used to manage advertisements. It assists in gathering data about consumer wants, desired products, and other topics. By putting the proper keyword in their writing, natural language processing aids businesses in reaching the right customer.

Challenges of Natural Language Processing

• Even today other people find it challenging to determine what someone means when they make ambiguous statements. It is not an easy process for the NLP system to search for context that would help it understand the term.
• There are thousands of languages with different set of rules for grammar usage, misspellings caused by human error, words with multiple meanings, phrases with multiple intentions, sarcasm which are all issues to be considered while developing a NLP framework. We still have a ways to go in order to cover these issues.

Conclusion

Despite some limitations, natural language processing still offers many benefits to businesses of all sizes. As new techniques and technologies are developed on a regular basis, many of these barriers will be removed in the next years. Natural language processing-based machine learning techniques can be used to instantly analyze huge amounts of text for previously unattainable insights. One of the simplest methods to deal with these issues if you're managing an NLP-based project is to employ a variety of NLP tools that are already available and can assist you in overcoming some of these challenges rapidly. Make use of other people's labor and ideas to give your customers a better product.