The 10 Biggest Issues Facing Natural Language Processing

In the recent past, models dealing with Visual Commonsense Reasoning [31] and NLP have also been getting attention of the several researchers and seems a promising and challenging area to work upon. Hidden Markov Models are extensively used for speech recognition, where the output sequence is matched to the sequence of individual phonemes. HMM is not restricted to this application; it has several others such as bioinformatics problems, for example, multiple sequence alignment [128]. Sonnhammer mentioned that Pfam holds multiple alignments and hidden Markov model-based profiles (HMM-profiles) of entire protein domains. HMM may be used for a variety of NLP applications, including word prediction, sentence production, quality assurance, and intrusion detection systems [133]. Santoro et al. [118] introduced a rational recurrent neural network with the capacity to learn on classifying the information and perform complex reasoning based on the interactions between compartmentalized information.

It leverages AI to summarize information in real time, which users share via Slack or Facebook Messenger. Besides, it provides summaries of audio content within a few seconds and supports multiple languages. SummarizeBot’s platform thus finds applications in academics, content creation, and scientific research, among others.

Data Quality and Quantity

Bi-directional Encoder Representations from Transformers (BERT) is a pre-trained model with unlabeled text available on BookCorpus and English Wikipedia. This can be fine-tuned to capture context for various NLP tasks such as question answering, sentiment analysis, text classification, sentence embedding, interpreting ambiguity in the text etc. [25, 33, 90, 148]. BERT provides contextual embedding for each word present in the text unlike context-free models (word2vec and GloVe).

It also generates context and behavior-driven analytics and provides various unique communication and content-related metrics from vocal and non-verbal sources. This way, the platform improves sales performance and customer engagement skills of sales teams. The startup’s virtual assistant engages with customers over multiple channels and devices as well as handles various languages. Besides, its conversational AI uses predictive behavior analytics to track user intent and identifies specific personas. This enables businesses to better understand their customers and personalize product or service offerings. 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 reading, listening to, and utilizing a language.

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

This can be a significant limitation in applications such as language translation and summarization. NLP algorithms have difficulty in understanding humor and sarcasm, which are often conveyed through implicit meanings and contextual clues. This can lead to inaccurate nlp challenges sentiment analysis and inappropriate responses in conversational AI systems. Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai™, a next generation enterprise studio for AI builders.