Overcoming the Top 3 Challenges to NLP Adoption

Concept Challenges of natural language processing NLP

one of the main challenges of nlp is

In an information retrieval case, a form of augmentation might be expanding user queries to enhance the probability of keyword matching. Another major benefit of NLP is that you can use it to serve your customers in real-time through chatbots and sophisticated auto-attendants, such as those in contact centers. NLP also pairs with optical character recognition (OCR) software, which translates scanned images of text into editable content.


While spam filtering or part of speech tagging help in this interpretation, it is hit-and-miss. However, like many humans, most of these models fail to catch linguistic subtleties, such as context, idioms, irony, or sarcasm. Algorithm models like Bag-of-Words (which focuses on total summarization), n-grams, and Hidden Markov Models (HMM) could not adequately capture and decode the complexities of human speech in big data. Natural language processing (NLP) is a field at the intersection of linguistics, computer science, and artificial intelligence concerned with developing computational techniques to process and analyze text and speech. State-of-the-art language models can now perform a vast array of complex tasks, ranging from answering natural language questions to engaging in open-ended dialogue, at levels that sometimes match expert human performance. Open-source initiatives such as spaCy1 and Hugging Face’s libraries (e.g., Wolf et al., 2020) have made these technologies easily accessible to a broader technical audience, greatly expanding their potential for application.


While adding an entire dictionary’s worth of vocabulary would make an NLP model more accurate, it’s often not the most efficient method. This is especially true for models that are being trained for a more niche purpose. It takes natural breaks, like pauses in speech or spaces in text, and splits the data into its respective words using delimiters (characters like ‘,’ or ‘;’ or ‘“,”’).

Even with these challenges, there are many powerful computer algorithms that can be used to extract and structure from text. The CircleCI platform excels at integrating testing into the development process. Support for automated testing makes it easy to ensure code performs as expected before it goes to production. You can customize tests on the CircleCI platform using one of many third-party integrations called orbs. Another critical aspect of managing ML model deployment is maintaining consistency and reproducibility in the build environment. These properties prevent unexpected errors when restarting CI/CD jobs or migrating from one build platform to another.

Latest developments and challenges in NLP

Semantic analysis is analyzing context and text structure to accurately distinguish the meaning of words that have more than one definition. Data cleansing is establishing clarity on features of interest in the text by eliminating noise (distracting text) from the data. It involves multiple steps, such as tokenization, stemming, and manipulating punctuation. Data enrichment is deriving and determining structure from text to enhance and augment data.

By understanding the human language, NLP can answer very basic, lower-level questions and answer them on behalf of the team. Complex tasks within natural language processing include direct machine translation, dialogue interface learning, digital information extraction, and prompt key summarisation. This interdisciplinary field automates the key elements of human vision systems using sensors, smart computers, and machine learning algorithms. Computer vision is the technical theory underlying artificial intelligence systems’ capability to view – and understand – their surroundings.

In this section, we describe the main resources in NLP research and development, including software and scientific libraries, corpora, and hardware analysis for running large-scale state-of-the-art models, focusing on Transformers. In applications, Google’s Duplex was something we’d never seen before. Several Chinese companies have also developed very impressive simultaneous interpretation technology.

Sub word tokenization is similar to word tokenization, but it breaks individual words down a little bit further using specific linguistic rules. Because prefixes, suffixes, and infixes change the inherent meaning of words, they can also help programs understand a word’s function. This can be especially valuable for out of vocabulary words, as identifying an affix can give a program additional insight into how unknown words function.

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