Exploring the opportunities and challenges of NLP models in higher education: is Chat GPT a blessing or a curse?

Seunghak et al. [158] designed a Memory-Augmented-Machine-Comprehension-Network (MAMCN) to handle dependencies faced in reading comprehension. The model achieved state-of-the-art performance on document-level using TriviaQA and QUASAR-T datasets, and paragraph-level using SQuAD datasets. Fan et al. [41] introduced a gradient-based neural architecture search algorithm that automatically finds architecture with better performance than a transformer, conventional NMT models. In conclusion, NLP thoroughly shakes up healthcare by enabling new and innovative approaches to diagnosis, treatment, and patient care. While some challenges remain to be addressed, the benefits of NLP in healthcare are pretty clear. Healthcare data is often messy, incomplete, and difficult to process, so the fact that NLP algorithms rely on large amounts of high-quality data to learn patterns and make accurate predictions makes ensuring data quality critical.

Event discovery in social media feeds (Benson et al.,2011) [13], using a graphical model to analyze any social media feeds to determine whether it contains the name of a person or name of a venue, place, time etc. Relationship extraction is a revolutionary innovation in the field of natural language processing… Natural language processing (NLP) is a technology that is already starting to shape the way we engage with the world. With the help of complex algorithms and intelligent analysis, NLP tools can pave the way for digital assistants, chatbots, voice search, and dozens of applications we’ve scarcely imagined. One of the main challenges of NLP is finding and collecting enough high-quality data to train and test your models.

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Additionally, NLP technology is still relatively new, and it can be expensive and difficult to implement. Natural language processing is expected to become more personalized, with systems that can understand the preferences and behavior of individual users. Named Entity Recognition is the process of identifying and classifying named entities in text data, such as people, organizations, and locations. This technique is used in text analysis, recommendation systems, and information retrieval. Discourse analysis involves analyzing a sequence of sentences to understand their meaning in context.

NLP is a form of Artificial Intelligence (AI) which enables computers to understand and process human language. It can be used to analyze customer feedback and conversations, identify trends and topics, automate customer service processes and provide more personalized customer experiences. Although there is a wide range of opportunities for NLP models, like Chat GPT and Google Bard, there are also several challenges (or ethical concerns) that should be addressed. The accuracy of the system depends heavily on the quality, diversity, and complexity of the training data, as well as the quality of the input data provided by students. In previous research, Fuchs (2022) alluded to the importance of competence development in higher education and discussed the need for students to acquire higher-order thinking skills (e.g., critical thinking or problem-solving). The system might struggle to understand the nuances and complexities of human language, leading to misunderstandings and incorrect responses.

NLP APPLICATIONS ( Intermediate but reliable  ) –

A laptop needs one minute to generate the 6 million inflected forms in a 340-Megabyte flat file, which is compressed in two minutes into 11 Megabytes for fast retrieval. Our program performs the analysis of 5,000 words/second for running text (20 pages/second). Based on these comprehensive linguistic resources, we created a spell checker that detects metadialog.com any invalid/misplaced vowel in a fully or partially vowelized form. Finally, our resources provide a lexical coverage of more than 99 percent of the words used in popular newspapers, and restore vowels in words (out of context) simply and efficiently. Despite these challenges, businesses can experience significant benefits from using NLP technology.

This technology is also the driving force behind building an AI assistant, which can help automate many healthcare tasks, from clinical documentation to automated medical diagnosis.
Researchers are proposing some solution for it like tract the older conversation and all .
If the training data is not adequately diverse or is of low quality, the system might learn incorrect or incomplete patterns, leading to inaccurate responses.
Yet, in some cases, words (precisely deciphered) can determine the entire course of action relevant to highly intelligent machines and models.
NLP models useful in real-world scenarios run on labeled data prepared to the highest standards of accuracy and quality.
Ahonen et al. (1998) [1] suggested a mainstream framework for text mining that uses pragmatic and discourse level analyses of text.

The framework requires additional refinement and evaluation to determine its relevance and applicability across a broad audience including underserved settings. Overload of information is the real thing in this digital age, and already our reach and access to knowledge and information exceeds our capacity to understand it. This trend is not slowing down, so an ability to summarize the data while keeping the meaning intact is highly required. In clinical case research, NLP is used to analyze and extract valuable insights from vast amounts of unstructured medical data such as clinical notes, electronic health records, and patient-reported outcomes.

Examples of Natural Language Processing in Action

The more features you have, the more storage and memory you need to process them, but it also creates another challenge. The more features you have, the more possible combinations between features you will have, and the more data you’ll need to train a model that has an efficient learning process. That is why we often look to apply techniques that will reduce the dimensionality of the training data. Natural Language Processing (NLP) is a rapidly growing field that has the potential to revolutionize how humans interact with machines. In this blog post, we’ll explore the future of NLP in 2023 and the opportunities and challenges that come with it.

There are particular words in the document that refer to specific entities or real-world objects like location, people, organizations etc. To find the words which have a unique context and are more informative, noun phrases are considered in the text documents. Named entity recognition (NER) is a technique to recognize and separate the named entities and group them under predefined classes. But in the era of the Internet, where people use slang not the traditional or standard English which cannot be processed by standard natural language processing tools. Ritter (2011) [111] proposed the classification of named entities in tweets because standard NLP tools did not perform well on tweets. They re-built NLP pipeline starting from PoS tagging, then chunking for NER.

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NLP systems must be designed to protect patient privacy and maintain data security, which can be challenging given the complexity of healthcare data and the potential for human error. These insights can then improve patient care, clinical decision-making, and medical research. NLP can also help clinicians identify patients at risk of developing certain conditions or predict their outcomes, allowing for more personalized and effective treatment. Another use of NLP technology involves improving patient care by providing healthcare professionals with insights to inform personalized treatment plans.

However, as language databases grow and smart assistants are trained by their individual users, these issues can be minimized.
Named entity recognition (NER) is a technique to recognize and separate the named entities and group them under predefined classes.
Developing those datasets takes time and patience, and may call for expert-level annotation capabilities.
As most of the world is online, the task of making data accessible and available to all is a challenge.
This field is quite volatile and one of the hardest current challenge in  NLP .
HMM is not restricted to this application; it has several others such as bioinformatics problems, for example, multiple sequence alignment [128].

Some of the earliest-used machine learning algorithms, such as decision trees, produced systems of hard if–then rules similar to existing handwritten rules. The cache language models upon which many speech recognition systems now rely are examples of such statistical models. Earlier machine learning techniques such as Naïve Bayes, HMM etc. were majorly used for NLP but by the end of 2010, neural networks transformed and enhanced NLP tasks by learning multilevel features. Major use of neural networks in NLP is observed for word embedding where words are represented in the form of vectors. Initially focus was on feedforward [49] and CNN (convolutional neural network) architecture [69] but later researchers adopted recurrent neural networks to capture the context of a word with respect to surrounding words of a sentence. LSTM (Long Short-Term Memory), a variant of RNN, is used in various tasks such as word prediction, and sentence topic prediction.

Text cleaning tools¶

Today, computers interact with written (as well as spoken) forms of human language overcoming challenges in natural language processing easily. For example, when a student submits a response to a question, the model can analyze the response and provide feedback customized to the student’s understanding of the material. This feedback can help the student identify areas where they might need additional support or where they have demonstrated mastery of the material.

OpenAI is an AI research organization that is working on developing advanced NLP technologies to enable machines to understand and generate human language. Part-of-Speech (POS) tagging is the process of labeling or classifying each word in written text with its grammatical category or part-of-speech, i.e. noun, verb, preposition, adjective, etc. It is the most common disambiguation process in the field of Natural Language Processing (NLP). The Arabic language has a valuable and an important feature, called diacritics, which are marks placed over and below the letters of the word.