Natural Language Processing - Human-to-Machine Interaction
Artificial Intelligence (AI) has impacted our lives in more ways than one. We are all witness to how most routine tasks are being automated thanks to AI. But is AI limited to only automating routine tasks? Is creativity too far-fetched for AI?
Not anymore! Natural Language Processing, a subset of AI has now made it possible to generate text such as articles on a particular topic, speech transcripts that express some attitude, etc. automatically.
What is Natural Language Processing (NLP)?
Natural Language Processing is a branch of Artificial Intelligence that pertains to interactions between humans and computers using natural language.
Natural Language Processing is the automatic manipulation of a natural language like speech or text with the help of software.
Human language is complicated to interpret for a computer. This makes it difficult for NLP to implement it accurately. Therefore, developers create software using NLP algorithms that help NLP devices and systems understand human language. NLP algorithms are based on machine learning algorithms.
The art of converting data into meaningful text is one that we often turned to humans for, assuming a machine will never understand the depth and intricacies of human language. But that was in the past, as Natural Language Generation can convert structured data into the language! It does this by relying on Natural Language Processing algorithms. The ideal technological platform for this is Python, because of its multiple libraries supporting NLP as well as being easy to pick up and use for anyone!
Natural Language Toolkit (NLTK) Python is one of the leading platforms for working with human language data and Python.
How does NLP work?
The working of NLP can be understood as:
Converting unstructured language data into a form that computers can understand by applying computer algorithms that identify and extract natural language rules.
When text is provided to the computer, it collects the relevant information from it with the help of algorithms that extract the meaning of every sentence in the text.
Steps in NLP:
Step 1: Phonetics
It entails analyzing how words are pronounced in terms of a sequence of sounds.
Step 2: Morphological Analysis
Non-word tokens such as punctuations are separated from the words and individual words are analyzed into their components.
Step 3: Syntactic Analysis
It entails transforming a linear sequence of words into structures that show how the words relate to each other.
Step 4: Semantic Analysis
In this step, meanings are assigned to structures created by the syntactic analyzer.
Step 5: Discourse Integration
The meaning of the individual sentence may depend on the preceding sentence and it will influence the following sentence. The meaning of the sentence is comprehended considering these aspects.
Step 6: Pragmatic Analysis
It is the reinterpretation of the structure representing what was said to determine what was actually meant.
Natural Language Processing Techniques
NLP uses Syntactic analysis and Semantic analysis for processing language.
Syntactic Analysis
The syntactic analysis helps assess how the natural language aligns itself with grammatical rules. Computer algorithms apply grammatical rules to a set of words to derive meaning from them.
Some of the syntax techniques applied are:
- Lemmatization: It is the technique of reducing various inflected forms of a word into a single word that simplifies its analysis.
- Morphological Segmentation: It is the process of dividing words into individual units called morphemes for easy analysis.
- Word Segmentation: It is the technique of dividing the long continuous text into distinct units.
- Part-of-speech Tagging: It entails identifying a part of speech for every word.
- Parsing: NLP parsing undertakes the grammatical analysis of the sentence provided.
- Sentence Breaking: It is the placing of sentence boundaries on a large piece of text.
- Stemming: It entails cutting inflected words to their root form.
Semantic Analysis
Semantic analysis is applying computer algorithms to understand the meaning and interpretation of words and how sentences are structured. The objective of semantic analysis is to analyze the meaning conveyed by the text.
Techniques used for Semantic Analysis are:
Named Entity Recognition (NER): It is the process of identifying parts of the text that can be categorized into preset groups.
Word Sense Disambiguation: It entails giving meaning to a word based on the context.
Natural Language Generation: It is the process of deriving semantic intentions using databases and converting them into human language.
What is Natural Language Processing used for?
Some common applications of Natural Language Processing are:
- Applications such as Google Translate are used for language translation.
- NLP is used for checking grammatical accuracy of texts in Word Processors such as Microsoft Word and Grammarly.
- They are extensively used in call centers in Interactive Voice Response (IVR) applications to interact with users.
- Applications such as Siri, Alexa, Ok Google, etc. These voice assistants use Natural Language Understanding (NLU). They digest the spoken text, understand and give relevant replies.
Natural Language Processing has made it easier for humans to interact with machines and connected devices. Natural Language interface enables people to interact with any connected device or service in a human-like manner. Natural Language Interface is also known as conversational AI. It enables technology to understand complex sentences that may contain multiple requests or several pieces of information. It reacts based on the input command, creating value and enhancing user experience.
Also Read: Transform Business with Cognitive Technology
According to Wikipedia, Natural Language Interface (LUI or NLUI) is a type of computer-human interface where linguistic phenomena such as verbs, phrases, clauses act as UI controls for creating, selecting, and modifying data in software applications.
How does natural language interface work?
LUI provides a natural human-like interaction with any application. It eliminates the need for the study of the special syntax of queries. It facilitates the detailed or precise description of the requested information.
LUI transforms queries in natural language into Boolean queries. It expands them with different ways of combining and paraphrasing. It is also possible to customize LUI by expanding the original query with synonyms.
Uses of Natural Language Interface:
Ubiquity:
It is an add-on for Mozilla Firefox. It is a collection of quick and simple natural language derived commands which enable users to get information and relate it to current and other web pages.
Wolfram Alpha:
It is an online service that offers answers to factual queries by computing answers from structured data, unlike a search engine which provides a list of web pages that might contain the answer to the query.
Siri:
It is an intelligent, personal assistant application that works with the operating system iOS. Siri leverages NLP to make recommendations and answer queries.
The Bottomline
Businesses have so far been analyzing customer actions to devise their strategies. However, now there is a need to go beyond that and interpret customer sentiments to provide them a better experience. This is possible by leveraging appropriate technologies such as NLP to assess and evaluate customer sentiments from data sources such as social media and mobile data. Natural Language Processing has an important role in Data Science as it can extract valuable insights from text empowering businesses to leverage these insights for better customer experience.