6 Challenges and Risks of Implementing NLP Solutions

Their work was based on identification of language and POS tagging of mixed script. They tried to detect emotions in mixed script by relating machine learning and human knowledge. They have categorized sentences into 6 groups based on emotions and used TLBO technique to help the users in prioritizing their messages based on the emotions attached with the message. Seal et al. (2020) [120] 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.

Like the culture-specific parlance, certain businesses use highly technical and vertical-specific terminologies that might not agree with a standard NLP-powered model. Therefore, if you plan on developing field-specific modes with speech recognition capabilities, the process of entity extraction, training, and data procurement needs to be highly curated and specific. In the quest for highest accuracy, non-English languages are less frequently being trained. One solution in the open source world which is showing promise is Google’s BERT, which offers an English language and a single “multilingual model” for about 100 other languages.

Approaches: Symbolic, statistical, neural networks

Adding customized algorithms to specific NLP implementations is a great way to design custom models—a hack that is often shot down due to the lack of adequate research and development tools. If your models were good enough to capture nuance while translating, they were also good enough to perform the original task. But more likely, they aren’t capable of capturing nuance, and your translation will not reflect the sentiment of the original document.

Businesses of all sizes have started to leverage advancements in natural language processing (NLP) technology to improve their operations, increase customer satisfaction and provide better services. NLP is a form of Artificial Intelligence (AI) which enables computers to understand and process human language. It can be used to analyze customer feedback and conversations, identify trends and topics, automate customer service processes and provide more personalized customer experiences. A third challenge of NLP is choosing and evaluating the right model for your problem. There are many types of NLP models, such as rule-based, statistical, neural, or hybrid ones.

Overcoming the Top 3 Challenges to NLP Adoption

Xie et al. [154] proposed a neural architecture where candidate answers and their representation learning are constituent centric, guided by a parse tree. Under this architecture, the search space of candidate answers is reduced while preserving the hierarchical, syntactic, and compositional structure among constituents. The objective of this section is to present the various datasets used in NLP and some state-of-the-art models in NLP.

• Semantic search is an advanced information retrieval technique that aims to improve the accuracy and relevance of search results by…
• It stores the history, structures the content that is potentially relevant and deploys a representation of what it knows.
• Also, it can carry out repetitive tasks such as analyzing large chunks of data to improve human efficiency.
• This article contains six examples of how boost.ai solves common natural language understanding (NLU) and natural language processing (NLP) challenges  that can occur when customers interact with a company via a virtual agent).
• Artificial Intelligence Stack Exchange is a question and answer site for people interested in conceptual questions about life and challenges in a world where “cognitive” functions can be mimicked in purely digital environment.

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