Unit-3

Natural Language Processing

Q1) What is speech recognition?

A1) Speech recognition refers to a computer interpreting the words spoken by a person and converting them to a format that is understandable by a machine. Depending on the end-goal, it is then converted to text or voice or another required format.

Q2) Explain speech recognition in AI?

A2) Speech recognition AI applications have seen significant growth in numbers in recent times as businesses are increasingly adopting digital assistants and automated support to streamline their services. Voice assistants, smart home devices, search engines, etc are a few examples where speech recognition has seen prominence.

Q3) Explain natural language understanding?

A3) Natural language understanding (NLU) is a branch of natural language processing (NLP), which involves transforming human language into a machine-readable format.

With the help of natural language understanding (NLU) and machine learning, computers can automatically analyse data in seconds, saving businesses countless hours and resources when analysing troves of customer feedback.

Natural language understanding focuses on a machine’s ability to understand the human language. NLU refers to how unstructured data is rearranged so that machines may “understand” and analyze it.

Example Machine Translation (MT)

Accurately translating text or speech from one language to another is one of the toughest challenges of natural language processing and natural language understanding.

Using complex algorithms that rely on linguistic rules and AI machine training, Google Translate, Microsoft Translator, and Facebook Translation have become leaders in the field of “generic” language translation.

You can type text or upload whole documents and receive translations in dozens of languages using machine translation tools. Google Translate even includes optical character recognition (OCR) software, which allows machines to extract text from images, read and translate it.

Q4) Explain natural language generation?

A4) Natural Language Generation (NLG), a subcategory of Natural Language Processing (NLP), is a software process that automatically transforms structured data into human-readable text. Using NLG, Businesses can generate thousands of pages of data-driven narratives in minutes using the right data in the right format.

Working of NLG:

An automated text generation process involves 6 stages. Consider the example of robot journalist news on a football match:

Content Determination

The limits of the content should be determined. The data often contains more information than necessary. In football news example, content regarding goals, cards, and penalties will be important for readers. 

Data interpretation

The analyzed data is interpreted. Thanks to machine learning techniques, patterns can be recognized in the processed data.  This is where data is put into context. For instance, information such as the winner of the match, goal scorers & assisters, minutes when goals are scored are identified in this stage.

Document planning

In this stage, the structures in the data are organized with the goal of creating a narrative structure and document plan. Football news generally starts with a paragraph that indicates the score of the game with a comment that describes the level of intensity and competitiveness in the game, then the writer reminds the pre-game standings of teams, describes other highlights of the game in the next paragraphs, and ends with player and coach interviews. 

Sentence Aggregation

It is also called micro planning, and this process is about choosing the expressions and words in each sentence for the end-user. In other words, this stage is where different sentences are aggregated in context because of their relevance

Grammaticalization

Grammaticalization stage makes sure that the whole report follows the correct grammatical form, spelling, and punctuation. This includes validation of actual text according to the rules of syntax, morphology, and orthography For instance, football games are written in the past tense.

Language Implementation

This stage involves inputting data into templates and ensuring that the document is output in the right format and according to the preferences of the user. 

Q5) What are chatbots?

A5) A chatbot is an artificial intelligence (AI) software that can simulate a conversation or a chat with a user in natural language through messaging applications, websites, mobile apps or through the telephone.
A chatbot is often described as one of the most advanced and promising expressions of interaction between humans and machines. However, from a technological point of view, a chatbot only represents the natural evolution of a Question Answering system leveraging Natural Language Processing (NLP). Formulating responses to questions in natural language is one of the most typical Examples of Natural Language Processing applied in various enterprises’ end-use applications.

There are two different tasks at the core of a chatbot:

Chatbot applications streamline interactions between people and services, enhancing customer experience. At the same time, they offer companies new opportunities to improve the customers engagement process and operational efficiency by reducing the typical cost of customer service.

Q6) Explain Machine Translation?

A6) Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).

To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.

Q7) What are the advantages of machine translation?

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Q8) What is machine translation used for?

A8) Machine Translation (MT) is an automated translation of text performed by a computer. It provides text translations based on computer algorithms without human involvement. With Machine Translation, source text is easily and quickly translated into one or more target languages.

Q9) What are the applications of speech recognition?

A9) Workplace

Speech recognition technology in the workplace has evolved into incorporating simple tasks to increase efficiency, as well as beyond tasks that have traditionally needed humans, to be performed.

Examples of office tasks digital assistants are, or will be, able to perform:

Banking

The aim of the banking and financial industry is for speech recognition to reduce friction for the customer. Voice-activated banking could largely reduce the need for human customer service, and lower employee costs. A personalised banking assistant could in return boost customer satisfaction and loyalty.

Marketing

Voice-search has the potential to add a new dimension to the way marketers reach their consumers. With the change in how people are going to be interacting with their devices, marketers should look for developing trends in user data and behaviour.

Q10) Explain the advantages of natural language understanding?

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