Natural Language Query Processing, or NLQP for short, refers to a subfield of artificial intelligence that focuses on handling interactions with computers through human language. These systems involve the digital understanding, interpretation, and manipulation of human language by computers resulting in easier, more natural, human-computer interactions.

• Improved User Experience: NLQP makes interactions with technical interfaces more instinctive for non-tech savvy users. This is because the interface language models are trained to understand and respond to human language.
• Advanced Machine Learning: NLQP heavily relies on machine learning techniques, such as neural networks, to process and interpret human language. This technology allows the system to learn from experience, improve its performance, and become more effective in understanding complex queries.
• Contextual Understanding: NLQP systems can comprehend the context of queries by recognizing the relevance of specific terms and the relationships between them. This feature contributes towards providing more accurate results.
• Real-time Processing: The NLQP systems are designed to provide instant responses by processing natural language-based queries in real-time.
• Adaptability: They get smarter with every interaction, refining their ability to understand and interpret complex and diverse linguistic patterns.

Numerous industries, such as e-commerce, customer service, and medical diagnosis, employ Natural Language Query Processing. It makes information retrieval more seamless and efficient, drastically reducing the time taken to find data or resolve issues.

NLQP provides multiple benefits, including:

• Simplified Interactions: NLQP simplifies users’ interactions with machines by allowing them to use natural, everyday language. This eradicates the need for users to learn complex query languages.
• Enhanced Efficiency: By making database querying more intuitive, NLQP enhances organisational productivity and efficiency. It aids in receiving speedy and pertinent results to complex queries.
• Reduces Errors: NLQP reduces misunderstandings and misinterpretations caused by technical jargon, reducing the time spent on error correction.
• Provides Competitive Advantage: A business adopting NLQP can offer superior customer service and analytics, providing them with a competitive advantage.
• Saves Time: NLQP can drastically reduce the time spent looking up information or finding data by producing precise and timely results.

Despite its myriad advantages, there are certain drawbacks to consider:

• Loss in Translation: Certain nuances and contextual meanings might be lost as machines are still trying to understand the complexities around multiple languages.
• Limited Understanding: NLQP systems may fail to fully understand highly technical, industry-specific lexicon and symbolic systems.
• Scalability: NLQP systems require constant updates and refinements to adapt to changing language patterns, dialects, and terminology.
• Security: Concerns around the misuse of data and privacy violations arise due to the extensive amount of data collected and processed by NLQP systems.

Implementing a successful NLQP system requires a well-planned strategy. This involves defining the system’s use case, understanding the end-users’ language structure, and testing the system’s capability of understanding and accurately processing the queries. Regular updates and refinements are also necessary to ensure the system keeps updating its language understanding capability and can accommodate ever-evolving human languages.

In conclusion, NLQP is an exciting and progressive field in artificial intelligence. It not only ameliorates human-machine interactions but is also beneficial for businesses looking to enhance their customer service, improve their analytics, and gain a competitive edge in the marketplace. However, its success heavily depends upon the quality and accuracy of its implementation.