Natural Language Processing, or NLP, is a branch of Artificial Intelligence (AI) whose focus is on facilitating the interaction between humans and machines by using natural human language as the interactive medium.
Systems based on NLP can understand the structure and meaning of human language by analyzing its various aspects such as syntax, semantics, and morphology. Using a combination of computer science and linguistics, natural language processing systems transform the knowledge they gain through the study of language into rules-based machine learning algorithms capable of solving specific problems and performing particular tasks.
NLP systems can perform large-scale analysis of unstructured data sets, encompassing text-based information drawn from online content, news reports, documents, social media commentary, and consumer interactions with brands and their customer support services. The tools used in natural language processing can enable machines to sift through this information and learn from it with minimal human interaction. In addition, systems can be custom-tailored to the needs of any industry or application.
Typical NLP applications include email filtering, voice assistants, translation systems, speech recognition, the automation of customer support, and the analysis and correction of written or spoken text. In addition, natural Language Processing is the driving force behind everyday use cases like chatbots, internet search engines, product recommendation portals, and the digital voice assistants that power smart homes, smart vehicles, and smartphones.
Use of NLP in Education
Much of the communication and activity that takes place in an educational context occurs through speech and text. This makes natural language processing and its use in education a logical match. NLP has been delivering benefits in an academic setting now, and new use cases are being developed and proposed.
Text Summarization
For large pieces of unstructured information such as dissertations and academic papers, automatic summarization under NLP makes it possible to reduce the text down to its essential elements and create a concise new version that conveys only the most relevant data. This process in NLP is known as extraction-based summarization and is used to create a summary of a given body of work.
In abstraction-based summarization, natural language processing employs deep learning techniques to unstructured information to paraphrase the existing text and to generate passages or sentences that were not present in the source document.
You can use automatic summarization at the research or documentation level to extract the most relevant information from a data resource and automatically enter the results into your research document or database.
Machine Translation (MT)
With the internet continually breaking down the barriers between nations, natural language processing tools for machine translation (MT) use deep learning neural systems to translate speech and text into different languages. Generic platforms for machine translation include Google Translate, Microsoft Translator, and the Facebook Translation App.
For more specialized or specific educational applications, there are customizable machine translation systems using NLP. These systems are tailored for a particular language domain or may be trained to understand the terminology and language associated with a specific profession, such as law, finance, or medicine.
Natural language platforms with machine translation capability can be particularly valuable to English Language Learners (ELLs), giving English- as-a-second-language students the opportunity for practice outside of the classroom. In addition, NLP systems with benchmarking facilities can help assess English language learners’ proficiency levels over time and track their progress. And online language tutors can provide feedback to these students about any grammatical, syntax, or sentence construction errors they make.
Using NLP to Improve Academic Writing
Historically, Natural Language Processing has had considerable success in the educational sphere, identifying student grammar and word mechanics problems and providing holistic scores for five-paragraph essays.
Beyond these relatively limited applications, natural language learning systems can assist students by providing actionable feedback on specific parts of a written body of text or formative feedback. Students can use this additional input to improve their writing range, beyond grammar and mechanics, when revising their work. For example, NLP analysis may reveal whether essential elements in an essay such as key topics, arguments, or evidence are present or absent in a student’s work. NLP systems can also give recommendations about the organization of a written work.
In combination with Automatic Writing Evaluation or AWE systems, Natural Language Processing solutions can furnish students with low-level feedback such as vocabulary tips or higher-level recommendations affecting the structure and flow of a document or narrative.
The range of writing tasks to which NLP may be applied continues to expand, including source-based essays. Writers must integrate data from multiple sources, external documents, narratives, and summaries.
Semantic and Sentiment Analysis in Learning Environments
The purpose of semantic analysis in NLP is to establish the meaning of language. It’s a complex area of natural language processing that requires systems to analyze sentence structure, the interactions between different words, and several related concepts. This analysis reveals the meaning of the words in a piece of text and the overall meaning and subject of the passage or document.
In sentiment analysis, natural language processing systems use machine learning models which can classify a piece of text according to the polarity of opinion that it expresses. The analysis classifies passages on a scale ranging from positive, negative, or neutral to shades of opinion between these extremes.
In the enterprise and data analytics spheres, sentiment analysis is employed to gauge public opinion on social media and other platforms and assess a customer or user feedback from the various commerce and support channels that consumers use to interact with brands.
Administrators and staff at educational institutions can use NLP semantic and sentiment analysis to study students’ behavior in response to the instruction they’re currently receiving and to changes in their academic and social environments. This can be instrumental in judging whether a particular curriculum or teaching approach is being well received and in identifying students who may be having issues of one kind or another.
Educators can also employ Natural Language Processing techniques to study the level of collaboration between students in a classroom. Researchers have begun to apply social network analysis approaches to language data in order to reveal patterns of collaboration between students in online discussion forums and within Massive Open Online Courses or MOOCs..
Establishing Readability Formulas
Using NLP analysis, educators can now establish readability formulas that help in matching reading materials to individual students in such a way that the text is suitably challenging and rewarding. The formulas make use of metrics providing information about language complexity in terms of vocabulary, text cohesion, and syntactic density.
Readability formula analysis enables instructors to better predict the rates at which students will be able to read and comprehend particular passages and are applicable to a variety of different readers and genres. Some NLP systems even feature simplification algorithms that can automatically modify text to make it a better fit for the students who will be reading it.
Future Hopes for Natural Language Generation (NLG)
Natural Language Generation or NLG is a sub-branch of NLP whose focus is on constructing computer systems and applications that can generate various kinds of text in natural language based on input from a semantic representation of meaning. Natural Language Processing systems with NLG currently use it to summarize text and generate answers to questions from the user.
The technology is still in its relative infancy, but in 2019 the artificial intelligence firm Open AI released GPT-2, a Natural Language Generation system with a training base of eight million web pages. The system can produce high-quality and cohesive text passages such as poems, stories, and news articles, even with minimal prompting. The NLG model performs better when fed with topics that have a high presence in its database and currently has issues in dealing with highly technical content or niche subject matter. However, there are high hopes for the future evolution and use of NLG technology in educational settings.
