Research and developments in science & technology remain one of humanity’s most significant achievements. Science and tech have allowed humans to explore beyond the farthest frontiers into outer space and beneath the deepest points in our oceans. We can now pay paper writers online, avail essay & assignment help services via the Web, and use plagiarism checker software to scan texts instantly.
And, it is through the auspices of technology and man’s evolved intellect, intuition & resolve that he is trying to replicate natural intelligence in machines.
The Ideas Behind AI Essay Writers
Artificial Intelligence is taking the world by storm and is currently one of the most advanced scientific research & development subjects.
First conceptualized in the 1960s, the term ‘AI’ was coined by the late great John McCarthy, Professor Emeritus of Computer Science at Stanford University. Since then, some of the most brilliant human minds have racked their gray cells and toiled incessantly to develop a concept into one of the most innovative & ground-breaking disciplines today. Meteoric advancements in electronics and software technology in the 21st century propelled AI exponentially, and today, it is on the verge of becoming ubiquitous. From smart devices to chatbots to computer games, automated grammar checkers & plagiarism checker tools, and robot essay writers, AI lies behind the scenes of many applications that we use today.
This article offers a glimpse into one of the most famous & powerful AI tools on the Web today, automated ghost writer or AI article spinners.
The Mechanism Behind AI Essay Writers
AI essay writers and article spinners are quite moderately available on the Internet today, reflecting on the proliferation of AI and its numerous subsets in recent times. Various online assignment and essay help services are the most common hosts of automated AI essay writers online. Nevertheless, the architecture of all AI essay writers employs a branch of Artificial Intelligence known as Natural Language Processing.
Natural Language Processing and Generation
Natural Language Processing (NLP) is a dedicated branch of Artificial Intelligence that merges computational linguistics, computer science, and data science to analyze natural languages and enable machines to learn natural human languages. NLP focuses on using machine learning & deep learning techniques to process and convert unstructured data such as natural languages into a structured format for better manipulation. It does so through several different processes such as:
- Named Entity Recognition that helps to identify named entities in a text,
- Identifying word patterns
- Using methods like tokenization, stemming, lemmatization for examining the root form of words.
Natural Language Generation (NLG) is a subset of natural language processing that enables machines to write and generate text. NLG involves the process of human-readable text response based on some input.
NLG comprises several text summarization techniques that generate summaries from input documents without hampering the integrity of its information. Hidden Markov Chains, Recurrent Neural Networks, Transformers, etc. Fro better dynamic real-time text generation.
NLG Architecture
Like complex natural language generation systems, a software application cannot be designed as a monolithic program. The architecture of an AI essay writer NLG system consists of component modules necessary for different types of processing. NLG systems are easier to design, construct and debug if decomposed into distinct, well-defined, and easily integrable modules. In addition, modular systems make reusing and modifying components of a plan easier.
Modularization of a typical AI-essay writer system involves multiple components where one particular module is responsible for selecting the information content from a specific source. At the same time, the other is responsible for expressing that information in a specific natural language.
AI Essay Writer: Inputs & Outputs
So, where does a natural language generation system start from? The ease or difficulty of the generation tasks depends primarily on the complexity of converting the input into the desired output.
Generally, a single input invocation in any NLG system or component in a more extensive system consists of a four-tuple <k, c, u, d> where k is the KNOWLEDGE SOURCE, C is the COMMUNICATIVE GOAL, U is the USER MODEL, and d is a DISCOURSE HISTORY.
- The KNOWLEDGE SOURCE is the information about the domain encoded in a database or data warehouses, which is available to the NLG system. The nature of information is generally application-dependent, and thus, a generalization of a knowledge source is nigh impossible.
- The COMMUNICATIVE GOAL describes the purpose of the text to be generated.
- The USER MODELcharacterizes the intended audience for whom the text is to be generated. User models are also not defined explicitly and depend upon the context of the concerned application.
- The DISCOURSE HISTORY of an NLG model is the text generated by it so far.
The discourse history helps the model keep track of already-mentioned entities & properties and allows it to speed up subsequent generations. The discourse history is used by AI essay writers to appropriately refer to entities & concepts already mentioned in the previous or current text or note information in a new text that has been already mentioned before.
The output of any NLG process is a TEXT, which can be either be read on paper, viewed online, or listened to. NLG systems are not concerned with the details of formatting of the generated output; they are integrated with DOCUMENT PRESENTATION SYSTEMS that process the linear sequence of word tokens, punctuation, and mark-up symbols generated by NLG systems.
Brief Overview Of NLG Modules & Their Tasks
Many decisions and considerations need to be addressed while devising the many processes necessary to generate the output. Therefore, the decision-making processes need to be divided into different tasks and modules; in the architecture to be discussed, the generation process has been decomposed into three primary component modules: The Document Planner, The Micro-planner, and The Surface Realizer.
(It should be noted that this is not the only way to build an NLG powered AI essay writer, but almost these three modules are integral to nearly every NLG model.)
- DOCUMENT PLANNER= Specifies content and structure of the output document using domain & application knowledge regarding the most appropriate information about the specified communicative goal, user model, etc.
- MICRO-PLANNER= Works on the linguistic and written aspects of the output text, that is, the best way to represent information in sentences;
- SURFACE-REALIZER= Focuses on the grammatical structure of the generated language;
A better way to understand the purpose of each module is by subdividing the tasks and processes attended by each. In a practical system, these tasks may not be separated into distinct software components but instead, be quite interleaved. All functions carried out by modules are primarily concerned with the content & structure of the output text.
MODULE CONTENT TASK STRUCTURE TASK
Document Planning –> Content Determination Document Structuring
Microplanning –> Lexicalization, Referring Aggregation
Expression Generation
Realization –> Linguistic Realization Structure Realization
Structure & Content Tasks
- Content Determination is the task o ascertaining what information is relevant to the NLG task, what output the user should receive. Thus, it is akin to the content aspect of document planning.
- Document Structuring is the decision-making process where the AI essay writer decides how the content will be presented or grouped in a document and how different chunks of content should be related in rhetorical terms. This step is similar to the structural aspect of document planning.
- Lexicalization constitutes the syntactical aspects of content selected at the content determination stage.
- Referring Expression Generation involves deciding upon the expressions to refer to entities and is a content task of the micro-planning module.
- Aggregation is the structure-related task of micro-planning, where decisions regarding the mapping of document structures to linguistic structures are taken.
- Linguistic Realization converts abstract representations of sentences into actual text and is a content aspect of the surface realization module.
- Structure Realization involves converting abstract document structures such as paragraphs and sections into mark-up symbols that the document presentation component of the NLG system will understand.
The above steps are implemented in software using various algorithms which interpret, transform and then process data.
Different NLP algorithms exist that employ different approaches for different kinds of language tasks. For example, hidden Markov Chains, N-Grams, Deep learning tactics such as Recurrent Neural Networks, etc., are employed by NLP models in chatbots, speech recognition systems, etc. Automated plagiarism checkers and AI essay paper help writers use NLP to convert unstructured input text into structured data for processing.
Unfortunately, that’s all the space we have for today. Hope this was an exciting and informative read for all readers alike. Come back here next time for more interesting articles n a variety of subjects and domains.
