11 Real-Life Examples of NLP in Action

problems with nlp

On the other hand, we might not need agents that actually possess human emotions. Stephan stated that the Turing test, after all, is defined as mimicry and sociopaths—while having no emotions—can fool people into thinking they do. We should thus be able to find solutions that do not need to be embodied and do not have emotions, but understand the emotions of people and help us solve our problems. Innate biases vs. learning from scratch   A key question is what biases and structure should we build explicitly into our models to get closer to NLU. Similar ideas were discussed at the Generalization workshop at NAACL 2018, which Ana Marasovic reviewed for The Gradient and I reviewed here.

By capturing relationships between words, the models have increased accuracy and better predictions. The transformer model processes the input sequence in parallel, so that lacks the inherent understanding of word order like the sequential model recurrent neural networks (RNNs), LSTM possess. Human language is filled with ambiguities that make it incredibly difficult to write software that accurately determines the intended meaning of text or voice data. Natural language processing (NLP) is a branch of artificial intelligence within computer science that focuses on helping computers to understand the way that humans write and speak. Natural language capabilities are being integrated into data analysis workflows as more BI vendors offer a natural language interface to data visualizations.

Understand what you need to measure

This is the limitation of BERT as it lacks in handling large text sequences. Natural Language Processing can be applied into various areas like Machine Translation, Email Spam detection, Information Extraction, Summarization, Question Answering etc. Next, we discuss some of the areas with the relevant work done in those directions. To generate a text, we need to have a speaker or an application and a generator or a program that renders the application’s intentions into a fluent phrase relevant to the situation. The NLP philosophy that we can ‘model’ what works from others is a great idea. But when you simply learn the technique without the strategic conceptualisation; the value in the overall treatment schema; or the potential for harm – then you are being given a hammer to which all problems are just nails.


Much of the current state of the art performance in NLP requires large datasets and this data hunger has pushed concerns about the perspectives represented in the data to the side. It’s clear from the evidence above, however, that these data sources are not “neutral”; they amplify the voices of those who have historically had dominant positions in society. The input gate controls how much new information should be stored in the memory cell.

What is the GRU model in NLP?

Hidden Markov Model is a probabilistic model based on the Markov Chain Rule used for modelling sequential data like characters, words, and sentences by computing the probability distribution of sequences. The main difference between a word-level and a character-level language model is how text is represented. A character-level language model represents text as a sequence of characters, whereas a word-level language model represents text as a sequence of words. GPT models are built on the Transformer architecture, which allows them to efficiently capture long-term dependencies and contextual information in text.

With this, companies can better understand customers’ likes and dislikes and find opportunities for innovation. Text classification is one of the most common applications of NLP in business. But for text classification to work for your company, it’s critical to ensure and storing the right data. OpenAI’s GPT-3 — a language model that can automatically write text — received a ton of hype this past year. For NLP, with the transformer architecture, we seem to have found an architecture that induces knowledge about the problem domain while at the same time being able to make use of the computing platforms we have today.

Title:Are NLP Models really able to Solve Simple Math Word Problems?

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. A first step is to understand the types of errors our model makes, and which kind of errors are least desirable. In our example, false positives are classifying an irrelevant tweet as a disaster, and false negatives are classifying a disaster as an irrelevant tweet. If the priority is to react to every potential event, we would want to lower our false negatives.

  • In other contexts, such as a chat bot, the lookup may involve using a database to match intent.
  • Multi-document summarization and multi-document question answering are steps in this direction.
  • IBM has launched a new open-source toolkit, PrimeQA, to spur progress in multilingual question-answering systems to make it easier for anyone to quickly find information on the web.
  • This is why researchers allocate significant resources towards curating datasets.

We next discuss some of the commonly used terminologies in different levels of NLP. A system can recognize words, phrases, and concepts based on NLP algorithms, which enable it to interpret and understand natural language. A computer model can be used to determine the context and meaning of a word, phrase, or sentence based on its context and meaning.

For more on NLP

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problems with nlp