Unit - 3
Natural Language Processing
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.
For instance, Apple’s Siri and Google’s Alexa use AI-powered speech recognition to provide voice or text support whereas voice-to-text applications like Google Dictate transcribe your dictated words to text. Voice recognition is another form of speech recognition where a source sound is recognized and matched to a person’s voice.
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.
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.
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.
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.
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.
References:
1. Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, Prentice Hall
2. Artificial Intelligence by Kevin Knight, Elaine Rich, Shivashankar B. Nair, Publisher: McGraw
Hill
3. Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, Jian Pei,
Publisher: Elsevier Science.
4. Speech & Language Processing by Dan Jurafsky, Publisher: Pearson Education
5. Neural Networks and Deep Learning A Textbook by Charu C. Aggarwal, Publisher: Springer
International Publishing
6. Introduction to Artificial Intelligence By Rajendra Akerkar, Publisher: PHI Learning
