Kampala High School

Welcome to Kampala High School Website

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis IEEE Journals & Magazine

nlp challenges

As the field continues to evolve and new technologies are developed, these challenges will need to be addressed to enable more sophisticated and effective NLP systems. There are other, smaller-scale initiatives that can contribute to creating and consolidating an active and diverse humanitarian NLP community. Compiling and sharing lists of educational resources that introduce NLP experts to the humanitarian world—and, vice versa, resources that introduce humanitarians to the basics of NLP—would be a highly valuable contribution.


Hugging Face is an open-source software library that provides a range of tools for natural language processing (NLP) tasks. The library includes pre-trained models, model architectures, and datasets that can be easily integrated into NLP machine learning projects. Hugging Face has become popular due to its ease of use and versatility, and it supports a range of NLP tasks, including text classification, question answering, and language translation.


I am currently a member of the research laboratory MIRACL (Multimedia, Information Systems and Advanced Computing Laboratory). My research interests focus on artificial intelligence, data science, machine learning and natural language processing methods. Businesses of all sizes have started to leverage advancements in natural language processing (NLP) technology to improve their operations, increase customer satisfaction and provide better services. 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.

  • Reinforcement learning was also used in depression detection143,144 to enable the model to pay more attention to useful information rather than noisy data by selecting indicator posts.
  • Natural language processing is a subset of artificial intelligence that presents machines with the ability to read, understand and analyze the spoken human language.
  • It also learns the chronological emotional spectrum of a user by using BERT fine-tuned for emotions as well as a heterogeneous social network graph.
  • Ultimately, while implementing NLP into a business can be challenging, the potential benefits are significant.
  • The metric of NLP assess on an algorithmic system allows for the integration of language understanding and language generation.
  • It can understand and respond to complex queries in a manner that closely resembles human-like understanding.

Next application is the ability to automate medical diagnosis, enabling healthcare professionals to quickly and accurately diagnose patients. The algorithms can analyze large amounts of unstructured data, such as medical records and clinical notes, and identify patterns and relationships that can aid in diagnosis. However, as with any new technology, there are challenges to be faced in implementing NLP in healthcare, including metadialog.com data privacy and the need for skilled professionals to interpret the data. Homonyms – two or more words that are pronounced the same but have different definitions – can be problematic for question answering and speech-to-text applications because they aren’t written in text form. These are easy for humans to understand because we read the context of the sentence and we understand all of the different definitions.

Text Analysis with Machine Learning

Research conducted on social media data often leverages other auxiliary features to aid detection, such as social behavioral features65,69, user’s profile70,71, or time features72,73. There are a number of additional resources that are relevant to this class of applications. CrisisBench is a benchmark dataset including social media text labeled along dimensions relevant for humanitarian action (Alam et al., 2021). This dataset contains collections of tweets from multiple major natural disasters, labeled by relevance, intent (offering vs. requesting aid), and sector of interest. Lacuna Fund13 is an initiative that aims at increasing availability of unbiased labeled datasets from low- or middle-income contexts.

nlp challenges

End-to-end training and representation learning are the key features of deep learning that make it a powerful tool for natural language processing. It might not be sufficient for inference and decision making, which are essential for complex problems like multi-turn dialogue. Furthermore, how to combine symbolic processing and neural processing, how to deal with the long tail phenomenon, etc. are also challenges of deep learning for natural language processing. The most important component required for natural language processing and machine learning to be truly effective is the initial training data.

Natural Language Processing (NLP) – A Brief History

Like further technical forms of artificial intelligence, natural language processing, and machine learning come with advantages, and challenges. Speech recognition capabilities are a smart machine’s capability to recognize and interpret specific phrases and words from a spoken language and transform them into machine-readable formats. It uses natural language processing algorithms to allow computers to imitate human interactions, and machine language methods to reply, therefore mimicking human responses. One prominent example of a real-world application where deep learning has made a significant impact in the context of NLP is in the field of question-answering systems. NLP systems require domain knowledge to accurately process natural language data. To address this challenge, organizations can use domain-specific datasets or hire domain experts to provide training data and review models.

nlp challenges

Santoro et al. [118] introduced a rational recurrent neural network with the capacity to learn on classifying the information and perform complex reasoning based on the interactions between compartmentalized information. Finally, the model was tested for language modeling on three different datasets (GigaWord, Project Gutenberg, and WikiText-103). Further, they mapped the performance of their model to traditional approaches for dealing with relational reasoning on compartmentalized information. 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. So, for building NLP systems, it’s important to include all of a word’s possible meanings and all possible synonyms.

Challenges of NLP in healthcare

Natural language processing is expected to become more personalized, with systems that can understand the preferences and behavior of individual users. Discourse analysis involves analyzing a sequence of sentences to understand their meaning in context. This technique is used to understand how sentences are related to each other and to extract the underlying meaning of a text. Syntactic analysis is the process of analyzing the structure of a sentence to understand its grammatical rules. This involves identifying the parts of speech, such as nouns, verbs, and adjectives, and how they relate to each other.

How do you solve NLP problems?

  1. A clean dataset allows the model to learn meaningful features and not overfit irrelevant noise.
  2. Remove all irrelevant characters.
  3. Tokenize the word by separating it into different words.
  4. convert all characters to lowercase.
  5. Reduce words such as ‘am’, ‘are’ and ‘is’ to a common form.

Thus, semantic analysis is the study of the relationship between various linguistic utterances and their meanings, but pragmatic analysis is the study of context which influences our understanding of linguistic expressions. Pragmatic analysis helps users to uncover the intended meaning of the text by applying contextual background knowledge. TF-IDF algorithm finds application in solving simpler natural language processing and machine learning problems for tasks like information retrieval, stop words removal, keyword extraction, and basic text analysis. However, it does not capture the semantic meaning of words efficiently in a sequence. Completely integrated with machine learning algorithms, natural language processing creates automated systems that learn to perform intricate tasks by themselves – and achieve higher success rates through experience. The earliest natural language processing/ machine learning applications were hand-coded by skilled programmers, utilizing rules-based systems to perform certain NLP/ ML functions and tasks.


But still there is a long way for this.BI will also make it easier to access as GUI is not needed. Because nowadays the queries are made by text or voice command on smartphones.one of the most common examples is Google might tell you today what tomorrow’s weather will be. But soon enough, we will be able to ask our personal data chatbot about customer sentiment today, and how we feel about their brand next week; all while walking down the street. But with time the technology matures – especially the AI component –the computer will get better at “understanding” the query and start to deliver answers rather than search results.

  • Automatic grammar checking, which is the task of noticing and remediating grammatical language errors and spelling mistakes within the text, is another prominent component of NLP-ML systems.
  • The Centre d’Informatique Hospitaliere of the Hopital Cantonal de Geneve is working on an electronic archiving environment with NLP features [81, 119].
  • Homonyms – two or more words that are pronounced the same but have different definitions – can be problematic for question answering and speech-to-text applications because they aren’t written in text form.
  • Advances in deep learning methods have brought breakthroughs in many fields including computer vision113, NLP114, and signal processing115.
  • Depending on the type of task, a minimum acceptable quality of recognition will vary.
  • That is the key priority for most asset managers, but also improving performance.

Text data preprocessing in an NLP project involves several steps, including text normalization, tokenization, stopword removal, stemming/lemmatization, and vectorization. Each step helps to clean and transform the raw text data into a format that can be used for modeling and analysis. Merity et al. [86] extended conventional word-level language models based on Quasi-Recurrent Neural Network and LSTM to handle the granularity at character and word level. They tuned the parameters for character-level modeling using Penn Treebank dataset and word-level modeling using WikiText-103. NLP can also aid in identifying potential health risks and providing targeted interventions to prevent adverse outcomes. It can also be used to develop healthcare chatbot applications that provide patients with personalized health information, answer common questions, and triage symptoms.

natural language processing (NLP)

Such a conversational application can supplement existing mental health services and provide accessible and convenient support to a wider population. Deep learning techniques, such as neural networks, have been used to develop more sophisticated NLP models that can handle complex language tasks like natural language understanding, sentiment analysis, and language translation. HUMSET makes it possible to develop automated NLP classification models that support, structure, and facilitate the analysis work of humanitarian organizations, speeding up crisis response, and detection. More generally, the dataset and its ontology provide training data for general purpose humanitarian NLP models. The evaluation results show the promising benefits of this approach, and open up future research directions for domain-specific NLP research applied to the area of humanitarian response. A major challenge for these applications is the scarce availability of NLP technologies for small, low-resource languages.

  • There are different text types, in which people express their mood, such as social media messages on social media platforms, transcripts of interviews and clinical notes including the description of patients’ mental states.
  • Mental illnesses, also called mental health disorders, are highly prevalent worldwide, and have been one of the most serious public health concerns1.
  • We will explore the different techniques used in NLP and discuss their applications.
  • Although numerous studies have been published on the issue of restoring the omitted vowels in speech technologies, little attention has been given to this problem in papers dedicated to written Arabic technologies.
  • One of the consequences of this is that organizations are often hesitant around open sourcing.
  • Until recently, the conventional wisdom was that while AI was better than humans at data-driven decision making tasks, it was still inferior to humans for cognitive and creative ones.

Debiasing word embeddings,” in 30th Conference on Neural Information Processing Systems (NIPS 2016) (Barcelona). Toy example of distributional semantic representations, figure and caption from Boleda and Herbelot (2016), Figure 2, (with adaptations). On the left, a toy distributional semantic lexicon, with words being represented through 2-dimensional vectors. Semantic distance between words can be computed as geometric distance between their vector representations. Words with more similar meanings will be closer in semantic space than words with more different meanings.

What are main challenges of NLP?

  • Multiple intents in one question.
  • Assuming it understands context and has memory.
  • Misspellings in entity extraction.
  • Same word – different meaning.
  • Keeping the conversation going.
  • Tackling false positives.

Leave a Comment