The Open Problems and Solutions of Natural Language Processing

At later stage the LSP-MLP has been adapted for French [10, 72, 94, 113], and finally, a proper NLP system called RECIT [9, 11, 17, 106] has been developed using a method called Proximity Processing [88]. It’s task was to implement a robust and multilingual system able to analyze/comprehend medical sentences, and to preserve a knowledge of free text into a language independent knowledge representation [107, 108]. In the area of deep learning, Attention Mechanisms have been augmented into what is called Dynamic Memory Networks to achieve very competitive results, at the time of their publication.

More recently, a language model named BERT, developed by Google and also incorporating Attention, has been able to achieve state-of-the-art results on SQuAD, a question-answering dataset. Data availability   Jade finally argued that a big issue is that there are no datasets available for low-resource languages, such as languages spoken in Africa. If we create datasets and make them easily available, such as hosting them on openAFRICA, that would incentivize people and lower the barrier to entry. It is often sufficient to make available test data in multiple languages, as this will allow us to evaluate cross-lingual models and track progress. Another data source is the South African Centre for Digital Language Resources (SADiLaR), which provides resources for many of the languages spoken in South Africa. The good news is that NLP has made a huge leap from the periphery of machine learning to the forefront of the technology, meaning more attention to language and speech processing, faster pace of advancing and more innovation.

The 4 Biggest Open Problems in NLP

They do this by looking at the context of your sentence instead of just the words themselves. Humans produce so much text data that we do not even realize the value it holds for businesses and society today. We don’t realize its importance because it’s part of our day-to-day lives and easy to understand, but if you input this same text data into a computer, it’s a big challenge to understand what’s being said or happening. The sixth and final step to overcome NLP challenges is to be ethical and responsible in your NLP projects and applications. NLP can have a huge impact on society and individuals, both positively and negatively. Therefore, you should be aware of the potential risks and implications of your NLP work, such as bias, discrimination, privacy, security, misinformation, and manipulation.

• Though it has its limitations, it still offers huge and wide-ranging advantages to any business.
• The metric of NLP assess on an algorithmic system allows for the integration of language understanding and language generation.
• Another trend is the state of the art, current trends and challenges in NLP2.
• They tuned the parameters for character-level modeling using Penn Treebank dataset and word-level modeling using WikiText-103.

It can identify that a customer is making a request for a weather forecast, but the location (i.e. entity) is misspelled in this example. By using spell correction on the sentence, and approaching entity extraction with machine learning, it’s still able to understand the request and provide correct service. A human being must be immersed in a language constantly for a period of years to become fluent in it; even the best AI must also spend a significant amount of time natural language processing problems reading, listening to, and utilizing a language. If you feed the system bad or questionable data, it’s going to learn the wrong things, or learn in an inefficient way. 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.

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However, this is a major challenge for computers as they don’t have the same ability to infer what the word was actually meant to spell. They literally take it for what it is — so NLP is very sensitive to spelling mistakes. Conversational AI can extrapolate which of the important words in any given sentence are most relevant to a user’s query and deliver the desired outcome with minimal confusion. In the first sentence, the ‘How’ is important, and the conversational AI understands that, letting the digital advisor respond correctly. In the second example, ‘How’ has little to no value and it understands that the user’s need to make changes to their account is the essence of the question. In the event that a customer does not provide enough details in their initial query, the conversational AI is able to extrapolate from the request and probe for more information.