The 10 Biggest Issues in Natural Language Processing NLP

Seal et al. proposed an efficient emotion detection method by searching emotional words from a pre-defined emotional keyword database and analyzing the emotion words, phrasal verbs, and negation words. Their proposed approach exhibited better performance than recent approaches. Pragmatic level focuses on the knowledge or content that comes from the outside the content of the document.

  • In summary, we find a steady interest in clinical NLP for a large spectrum of languages other than English that cover Indo-European languages such as French, Swedish or Dutch as well as Sino-Tibetan , Semitic or Altaic languages.
  • This is where training and regularly updating custom models can be helpful, although it oftentimes requires quite a lot of data.
  • Our selection criteria were based on the IMIA definition of clinical NLP .
  • The CLEF-ER 2013 evaluation lab was the first multi-lingual forum to offer a shared task across languages.
  • Under this architecture, the search space of candidate answers is reduced while preserving the hierarchical, syntactic, and compositional structure among constituents.
  • For example, by some estimations, (depending on language vs. dialect) there are over 3,000 languages in Africa, alone.

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. Today, NLP tends to be based on turning natural language into machine language. 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. Initially, the data chatbot will probably ask the question ‘how have revenues changed over the last three-quarters? But once it learns the semantic relations and inferences of the question, it will be able to automatically perform the filtering and formulation necessary to provide an intelligible answer, rather than simply showing you data.

NLP based Deep Learning Approach for Plagiarism Detection

If you’re working with NLP for a project of your own, one of the easiest ways to resolve these issues is to rely on a set of NLP tools that already exists—and one that helps you overcome some of these obstacles instantly. Use the work and ingenuity of others to ultimately create a better product for your customers. Some phrases and questions actually have multiple intentions, so your NLP system can’t oversimplify the situation by interpreting only one of those intentions.

The second problem is that with large-scale or multiple documents, supervision is scarce and expensive to obtain. We can, of course, imagine a document-level unsupervised task that requires predicting the next paragraph or deciding which chapter comes next. A more useful direction seems to be multi-document summarization and multi-document question answering.

Higher-level NLP applications

Some of the work in languages other than English addresses core NLP tasks that have been widely studied for English, such as sentence boundary detection , part of speech tagging [28–30], parsing , or sequence segmentation . Word segmentation issues are more obviously visible in languages which do not mark word boundaries with clear separators such as white spaces. This is the case, for instance, in Chinese, Japanese, Vietnamese and Thai.

nlp technology

A study in 2019 used BERT to address the particularly difficult challenge of argument comprehension, where the model has to determine whether a claim is valid based on a set of facts. BERT achieved state-of-the-art performance, but on further examination it was found that the model was exploiting particular clues in the language that had nothing to do with the argument’s “reasoning”. Along similar lines, you also need to think about the development time for an NLP system.

Techniques and methods of natural language processing

What should be learned and what should be hard-wired into the model was also explored in the debate between Yann LeCun and Christopher Manning in February 2018. 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. Usage of their and there, for example, is even a common problem for humans. Machine Learning vs NLP – Understand what is the difference between machine learning and NLP and how they relate to each other. This tutorial will walk you through the key ideas of deep learning programming using Pytorch. Many of the concepts are not unique to Pytorch and are relevant to any deep learning toolkit out there.

neural machine translation

To solve this problem, nlp problems offers several methods, such as evaluating the context or introducing POS tagging, however, understanding the semantic meaning of the words in a phrase remains an open task. Endeavours such as OpenAI Five show that current models can do a lot if they are scaled up to work with a lot more data and a lot more compute. With sufficient amounts of data, our current models might similarly do better with larger contexts.

Neural Abstractive Text Summarization with Sequence-to-Sequence Models

After training the same model a third time , we get an accuracy score of 77.7%, our best result yet! However, it is very likely that if we deploy this model, we will encounter words that we have not seen in our training set before. The previous model will not be able to accurately classify these tweets, even if it has seen very similar words during training. Confusion Matrix Our classifier creates more false negatives than false positives . In other words, our model’s most common error is inaccurately classifying disasters as irrelevant. If false positives represent a high cost for law enforcement, this could be a good bias for our classifier to have.


The workshop provided a beginner-friendly introduction to NLP and ASR, including a step by step guide on how to train a speech model for a new language. Participants also learned about the challenges and progress of work in the Africa NLP space and opportunities to get involved with data science and grow their careers. Although there are doubts, natural language processing is making significant strides in the medical imaging field. Learn how radiologists are using AI and NLP in their practice to review their work and compare cases.