Natural Language Processing: Challenges and Future Directions SpringerLink

What are the Natural Language Processing Challenges, and How to Fix?

7 Major Challenges of NLP Every Business Leader Should Know

Following companies dedicated to NLP outside of English is one way to find sleeper hits within the field. It’s reportedly state of the art within Chinese language understanding and available on GitHub. It was able to improve accuracy substantially across three different NLP tasks and has far-reaching business applications in both sentiment analysis and abusive language detection.

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If you are an NLP practitioner, all problems look like a timeline therapy or a movie theatre, or (insert other favourite technique) solution. Vendors offering most or even some of these features can be considered for designing your NLP models. One example would be a ‘Big Bang Theory-specific ‘chatbot that understands ‘Buzzinga’ and even responds to the same. If you think mere words can be confusing, here are is an ambiguous sentence with unclear interpretations. A ‘Bat’ can be a sporting tool and even a tree-hanging, winged mammal. Despite the spelling being the same, they differ when meaning and context are concerned.

Increasing Customer Expectations

The IBM research showed that almost half of businesses are using applications powered by NLP and one in four businesses plan to begin using NLP technology over the next 12 months. These five organizations are using natural language processing to better serve their customers, automate repetitive tasks, and streamline operations. Multiple solutions help identify business-relevant content in feeds from SM sources and provide feedback on the public’s

opinion about companies’ products or services.

7 Major Challenges of NLP Every Business Leader Should Know

Machine learning requires A LOT of data to function to its outer limits – billions of pieces of training data. That said, data (and human language!) is only growing by the day, as are new machine learning techniques and custom algorithms. All of the problems above will require more research and new techniques in order to improve on them. The third step to overcome NLP challenges is to experiment with different models and algorithms for your project. There are many types of NLP models, such as rule-based, statistical, neural, and hybrid models, that have different strengths and weaknesses. For example, rule-based models are good for simple and structured tasks, but they require a lot of manual effort and domain knowledge.


The recent introduction of transfer learning and pre-trained language models to natural language processing has allowed for a much greater understanding and generation of text. Applying transformers to different downstream NLP tasks has become the primary focus of advances in this field. Data

generated from conversations, declarations, or even tweets are examples of unstructured data. Unstructured data doesn’t [newline]fit neatly into the traditional row and column structure of relational databases and represent the vast majority of data

available in the actual world. There is a significant difference between NLP and traditional machine learning tasks, with the former dealing with

unstructured text data while the latter usually deals with structured tabular data. Therefore, it is necessary to [newline]understand human language is constructed and how to deal with text before applying deep learning techniques to it.

It has future implications in the world of chatbots, customer comment classifications, and relevant document searches. NLP software is challenged to reliably identify the meaning when humans can’t be sure even after reading it multiple

times or discussing different possible meanings in a group setting. Irony, sarcasm, puns, and jokes all rely on this

natural language ambiguity for their humor.

Effective NLP models know when to query the customer for further information, drawing from a customer’s complete history with a business, and when to complete a task for a customer. Sophisticated NLP models should additionally know what policy constraints are in place, such as honoring a refund request when it is within a company return policy. Natural language processing, or NLP for short, is the automatic manipulation of natural language like speech and text by software.

7 Major Challenges of NLP Every Business Leader Should Know

The platform, known as Conversus, allows customer-centric organizations to utilize their data to extract deep, meaningful insights whether or not they have a dedicated data science team at their disposal. The Allen Institute brings you one of the most talked-about deep contextualized word representations, ELMo. The model achieved state of the art results and error reductions in a variety of NLP tasks, including question answering, named entity extraction, and semantic role labeling. It requires significantly fewer updates to achieve these state of the art results and (even better) requires significantly less training data.

Great Wolf Lodge tracks customer sentiment with NLP-powered AI

For example, the word “process” can be spelled as either “process” or “processing.” The problem is compounded when you add accents or other characters that are not in your dictionary. Businesses use it to improve the search on a website, run chatbots or analyze clients’ feedback. At the moment, scientists can quite successfully analyze a part of a language concerning one area or industry. There is still a long way to go until we will have a universal tool that will work equally well with different languages and accomplish various tasks. In relation to NLP, it calculates the distance between two words by taking a cosine between the common letters of the dictionary word and the misspelt word.

7 Major Challenges of NLP Every Business Leader Should Know

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