BEST ONLINE NLP COURSE INSTITUTES IN ERNAKULAM

 BEST ONLINE NLP COURSE INSTITUTES-TRANSORZE SOLUTIONS.

                                 

NATURAL LANGUAGE PROCESSING

 

The field of natural language processing (NLP) is one of the most important and useful application areas of artificial intelligence. NLP is undergoing rapid evolution as new methods and toolsets converge with an ever-expanding availability of data. In this course you will explore the fundamental concepts of NLP and its role in current and emerging technologies. You will gain a thorough understanding of modern neural network algorithms for the processing of linguistic information. By mastering cutting-edge approaches, you will gain the skills to move from word representation and syntactic processing to designing and implementing complex deep learning models for question answering, machine translation, and other language understanding tasks.

What you will learn

• Computational properties of natural languages
• Neural network models for language understanding tasks
• Word vectors, syntactic, and semantic processing
• Coreference, question answering, and machine translation
• Transformers and pretraining

 

Why is natural language processing important?

Businesses use massive quantities of unstructured, text-heavy data and need a way to efficiently process it. A lot of the information created online and stored in databases is natural human language, and until recently, businesses could not effectively analyze this data. This is where natural language processing is useful.

The advantage of natural language processing can be seen when considering the following two statements: "Cloud computing insurance should be part of every service-level agreement," and, "A good SLA ensures an easier night's sleep -- even in the cloud." If a user relies on natural language processing for search, the program will recognize that cloud computing is an entity, that cloud is an abbreviated form of cloud computing and that SLA is an industry acronym for service-level agreement.

 

What is natural language processing?

Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken and written -- referred to as natural language. It is a component of artificial intelligence (AI).

NLP has existed for more than 50 years and has roots in the field of linguistics. It has a variety of real-world applications in a number of fields, including medical research, search engines and business intelligence.

Techniques and methods of natural language processing

Syntax and semantic analysis are two main techniques used with natural language processing.

Syntax is the arrangement of words in a sentence to make grammatical sense. NLP uses syntax to assess meaning from a language based on grammatical rules. Syntax techniques include:

• Parsing. This is the grammatical analysis of a sentence. Example: A natural language processing algorithm is fed the sentence, "The dog barked." Parsing involves breaking this sentence into parts of speech -- i.e., dog = noun, barked = verb. This is useful for more complex downstream processing tasks.

• Word segmentation. This is the act of taking a string of text and deriving word forms from it. Example: A person scans a handwritten document into a computer. The algorithm would be able to analyze the page and recognize that the words are divided by white spaces.

• Sentence breaking. This places sentence boundaries in large texts. Example: A natural language processing algorithm is fed the text, "The dog barked. I woke up." The algorithm can recognize the period that splits up the sentences using sentence breaking.

• Morphological segmentation. This divides words into smaller parts called morphemes. Example: The word untestably would be broken into [[un[[test]able]]ly], where the algorithm recognizes "un," "test," "able" and "ly" as morphemes. This is especially useful in machine translation and speech recognition.

• Stemming. This divides words with inflection in them to root forms. Example: In the sentence, "The dog barked," the algorithm would be able to recognize the root of the word "barked" is "bark." This would be useful if a user was analyzing a text for all instances of the word bark, as well as all of its conjugations. The algorithm can see that they are essentially the same word even though the letters are different.

• Word sense disambiguation. This derives the meaning of a word based on context. Example: Consider the sentence, "The pig is in the pen." The word pen has different meanings. An algorithm using this method can understand that the use of the word pen here refers to a fenced-in area, not a writing implement.

• Named entity recognition. This determines words that can be categorized into groups. Example: An algorithm using this method could analyze a news article and identify all mentions of a certain company or product. Using the semantics of the text, it would be able to differentiate between entities that are visually the same. For instance, in the sentence, "Daniel McDonald's son went to McDonald's and ordered a Happy Meal," the algorithm could recognize the two instances of "McDonald's" as two separate entities -- one a restaurant and one a person.

• Natural language generation. This uses a database to determine semantics behind words and generate new text. Example: An algorithm could automatically write a summary of findings from a business intelligence platform, mapping certain words and phrases to features of the data in the BI platform. Another example would be automatically generating news articles or tweets based on a certain body of text used for training.

Current approaches to natural language processing are based on deep learning, a type of AI that examines and uses patterns in data to improve a program's understanding. Deep learning models require massive amounts of labeled data for the natural language processing algorithm to train on and identify relevant correlations, and assembling this kind of big data set is one of the main hurdles to natural language processing.

Earlier approaches to natural language processing involved a more rules-based approach, where simpler machine learning algorithms were told what words and phrases to look for in text and given specific responses when those phrases appeared. But deep learning is a more flexible, intuitive approach in which algorithms learn to identify speakers' intent from many examples -- almost like how a child would learn human language.

Three tools used commonly for natural language processing include Natural Language Toolkit (NLTK), Gensim and Intel natural language processing Architect. NLTK is an open source Python module with data sets and tutorials. Gensim is a Python library for topic modeling and document indexing. Intel NLP Architect is another Python library for deep learning topologies and techniques.

What is natural language processing used for?

Some of the main functions that natural language processing algorithms perform are:

• Text classification. This involves assigning tags to texts to put them in categories. This can be useful for sentiment analysis, which helps the natural language processing algorithm determine the sentiment, or emotion behind a text. For example, when brand A is mentioned in X number of texts, the algorithm can determine how many of those mentions were positive and how many were negative. It can also be useful for intent detection, which helps predict what the speaker or writer may do based on the text they are producing.
• Text extraction. This involves automatically summarizing text and finding important pieces of data. One example of this is keyword extraction, which pulls the most important words from the text, which can be useful for search engine optimization. Doing this with natural language processing requires some programming -- it is not completely automated. However, there are plenty of simple keyword extraction tools that automate most of the process -- the user just has to set parameters within the program. For example, a tool might pull out the most frequently used words in the text. Another example is named entity recognition, which extracts the names of people, places and other entities from text.
• Machine translation. This is the process by which a computer translates text from one language, such as English, to another language, such as French, without human intervention.
• Natural language generation. This involves using natural language processing algorithms to analyze unstructured data and automatically produce content based on that data. One example of this is in language models such as GPT3, which are able to analyze an unstructured text and then generate believable articles based on the text.

The functions listed above are used in a variety of real-world applications, including:

• customer feedback analysis -- where AI analyzes social media reviews;
• customer service automation -- where voice assistants on the other end of a customer service phone line are able to use speech recognition to understand what the customer is saying, so that it can direct the call correctly;
• automatic translation -- using tools such as Google Translate, Bing Translator and Translate Me;
• academic research and analysis -- where AI is able to analyze huge amounts of academic material and research papers not just based on the metadata of the text, but the text itself;
• analysis and categorization of medical records -- where AI uses insights to predict, and ideally prevent, disease;
• word processors used for plagiarism and proofreading -- using tools such as Grammarly and Microsoft Word;
• stock forecasting and insights into financial trading -- using AI to analyze market history and 10-K documents, which contain comprehensive summaries about a company's financial performance;
• talent recruitment in human resources; and
• automation of routine litigation tasks -- one example is the artificially intelligent attorney.

Research being done on natural language processing revolves around search, especially Enterprise search. This involves having users query data sets in the form of a question that they might pose to another person. The machine interprets the important elements of the human language sentence, which correspond to specific features in a data set, and returns an answer.

NLP can be used to interpret free, unstructured text and make it analyzable. There is a tremendous amount of information stored in free text files, such as patients' medical records. Before deep learning-based NLP models, this information was inaccessible to computer-assisted analysis and could not be analyzed in any systematic way. With NLP analysts can sift through massive amounts of free text to find relevant information.

Sentiment analysis is another primary use case for NLP. Using sentiment analysis, data scientists can assess comments on social media to see how their business's brand is performing, or review notes from customer service teams to identify areas where people want the business to perform better.