Explaining the language in Natural Language

Last Updated : 24 Jan, 2023

INTRODUCTION:

Natural language refers to the language that is used by humans to communicate with each other. This includes languages such as English, Spanish, Chinese, and many others. Natural language is characterized by its complexity, variability, and dynamic nature. It is also context-dependent, meaning that the meaning of words and phrases can change depending on the context in which they are used.

In the field of Natural Language Processing (NLP), the goal is to enable computers to understand and process natural language in a way that is similar to how humans process it. This involves analyzing and understanding the structure, meaning, and context of natural language text and speech.

NLP techniques are used to perform various language-related tasks, such as language translation, text summarization, sentiment analysis, and question answering. These techniques are based on a combination of rule-based systems, statistical models, and deep learning and neural networks.

The field of NLP is constantly evolving, with new techniques and technologies being developed to improve the ability of computers to understand and process natural language. The goal is to develop NLP systems that can understand and respond to natural language in a way that is similar to how humans do, which can enable new applications and use cases for NLP, such as chatbots, virtual assistants, and question answering systems.

We begin by defining the lexicon, or a list of terms that are permitted. The words are categorized into the lexical categories that dictionary users are familiar with: nouns, pronouns, and names to denote things; verbs to denote events; adjectives to modify nouns; adverbs to modify verbs; and function words: articles (such as the), prepositions (in), and conjunctions to modify verbs (and). A tiny vocabulary for the language $\mathcal{E}_{0}$ is shown below:

$\begin{array}{ll} \text { Noun } & \rightarrow \text { stench }[0.05] \mid \text { breeze }[0.10] \mid \text { wumpus }[0.15] \mid \text { pits }[0.05] \mid \ldots \\ \text { Verb } & \rightarrow \text { is }[0.10] \mid \text { feel }[0.10] \mid \text { smells }[0.10] \mid \text { stinks }[0.05] \mid \ldots \\ \text { Adjective } & \rightarrow \text { right }[0.10] \mid \text { dead }[0.05] \mid \text { smelly }[0.02] \mid \text { breezy }[0.02] \ldots \\ \text { Adverb } & \rightarrow \text { here }[0.05] \mid \text { ahead }[0.05] \mid \text { nearby }[0.02] \mid \ldots \\ \text { Pronoun } & \rightarrow \text { me }[0.10] \mid \text { you }[0.03] \mid \text { I }[0.10] \mid \text { it }[0.10] \mid \ldots \\ \text { RelPro } & \rightarrow \text { that }[0.40] \mid \text { which }[0.15] \mid \text { who }[0.20] \mid \text { whom }[0.02] \vee \ldots \\ \text { Name } & \rightarrow \text { John }[0.01] \mid \text { Mary }[0.01] \mid \text { Boston }[0.01] \mid \ldots \\ \text { Article } & \rightarrow \text { the }[0.40] \mid \text { a }[0.30] \mid \text { an }[0.10] \mid \text { every }[0.05] \mid \ldots \\ \text { Prep } & \rightarrow \text { to }[0.20] \mid \text { in }[0.10] \mid \text { on }[0.05] \mid \text { near }[0.10] \mid \ldots \\ \text { Conj } & \rightarrow \text { and }[0.50] \mid \text { or }[0.10] \mid \text { but }[0.20] \mid \text { yet }[0.02] \vee \ldots \\ \text { Digit } & \rightarrow \mathbf{0}[0.20]|\mathbf{1}[0.20]| \mathbf{2}[0.20]|\mathbf{3}[0.20]| \mathbf{4}[0.20] \mid \ldots \end{array}$

The terms RelPro, Prep, and Conj stand for relative pronoun, preposition, and conjunction, respectively. Each category’s probability adds up to one.

Each category ends with… to indicate that the category contains further terms. It is impossible to list all the terms for nouns, names, verbs, adjectives, and adverbs, even in theory. Not only do each class have tens of thousands of members, but new ones are continually being introduced, such as iPod or biodiesel. These five types of classes are referred to as open classes. With a little more effort, we could have listed all the terms in the categories of pronoun, relative pronoun, article, preposition, and conjunction. Closed courses are those that have a limited amount of words (a dozen or so). Closed classes evolve over millennia rather than months. “Thee” and “thou,” for example, were frequent pronouns in the 17th century, declined in the 19th, and are now only found in poetry and some regional dialects.

$\begin{array}{lclll} \mathcal{E}_{0}: & S & \rightarrow & N P V P & {[0.90]} & \mathrm{I}+\text { feel a breeze } \\ & & \mid & S \operatorname{Conj} S & {[0.10]} & \text { I feel a breeze }+\text { and }+\text { It stinks } \\ \end{array}$

$\begin{array}{lll} \text { NP } & \rightarrow \text { Pronoun } & {[0.30] \text { I }} \\ & \mid \text { Name } & {[0.10] \text { John }} \\ & \text { Noun } & {[0.10] \text { pits }} \\ & \text { Article Noun } & {[0.25] \text { the + wumpus }} \\ & \text { Article Adjs Noun }[0.05] \text { the + smelly dead + wumpus } \\ & \text { Digit Digit } & {[0.05] } 34 \\ & \text { NP PP } & {[0.10]} \text { the wumpus + in } 13 \\ & \text { NP RelClause } & {[0.05]} \text { the wumpus + that is smelly } \end{array}$

$\begin{aligned} &\begin{array}{rlll} V P & \rightarrow & \operatorname{Verb} & {[0.40]} & \text { stinks } \\ & \mid & V P N P & {[0.35]} & \text { feel + a breeze } \\ & \mid & V P \text { Adjective } & {[0.05]} & \text { smells + dead } \\ & V P P P & {[0.10]} & \text { is + in 1 3 } \\ & V P \text { Adverb } & {[0.10]} & \text { go + ahead } \end{array}\\ &\text { Adjs } \rightarrow \text { Adjective } \quad[0.80] \text { smelly } \end{aligned}$

The next stage is to put the words together in sentences. A grammar for $\mathcal{E}_{0}$ is shown in the equation above, with rules for each of the six syntactic categories and examples for each rewrite rule. 2 The parse tree for the line “Every wumpus stinks” is shown in the figure below. According to the criteria of E0, the parse tree provides constructive evidence that the string of words is really a sentence.

BENEFITS OF :Explaining the language in Natural Language

The benefits of explaining the language in natural language include:

Improved human-computer interaction: By using natural language to explain the language, it can improve the overall user experience and make it easier for people to interact with computers.
Enhancing the understanding: By explaining the language in natural language, it can make the language more understandable for people, which can lead to better communication and improved understanding.
Improving accessibility: By explaining the language in natural language, it can make technology more accessible for people with disabilities, such as those who are visually impaired or have cognitive difficulties.
Facilitating education: By explaining the language in natural language, it can make it easier for people to learn new languages and improve their language skills.
Enabling natural language based systems: By explaining the language in natural language, it can enable the development of natural language-based systems such as chatbot, virtual assistants, recommendation systems, and more.
Improving decision-making: By explaining the language in natural language, it can help to extract insights from large amounts of unstructured data, such as social media posts and customer feedback, which can improve decision-making in various industries.
Enhancing the naturalness of the communication : By explaining the language in natural language, it can make the communication more natural, which can lead to more accurate and personalized responses.