Natural Language Semantics, often simply referred to as Semantics, is the study of meaning communicated through language. It plays a vital part in the field of linguistics and natural language processing (NLP), focusing on deciphering the meanings behind words, phrases, sentences, and even larger units of spoken or written discourse.

• Complexity: Semantics studies meaning that can often be complex or multi-layered, representing the rich diversity and cognitive intricacies of human language.

• Interprets Context: Natural Language Semantics deals with interpretation of context and identification of particular concepts within a text.

• Structural Analysis: It involves thoroughly analyzing language structures and their semantic relationships to grasp their meaning within a unique system of rules and conventions.

• Linking Syntax to Meaning: Semantics connects the syntactic (structural) aspects of language to their associated meanings, thus decoding and interpreting natural human language.

• Understanding of Pragmatics: This involves examining the relationship between language and its context of use, such as speaker's intention and listener's interpretation.

Successful implementation of Natural Language Semantics, especially in the development of AI-controlled systems or language processing software, requires a deep understanding of both the language and the target audience’s cultural context. Employing skilled linguists or language processing experts, and using rigorous testing and fine-tuning processes, can further enhance the accuracy and effectiveness of the implemented systems.

Conclusively, despite its challenges, Natural Language Semantics remains an indispensable tool in linguistics and natural language processing. Its critical role in extracting meaning from language data elevates it as a significant field, essential to the development of AI-driven technology and communication. However, due to its complex nature, it requires an immense level of expertise and understanding to be harnessed effectively.

Semantics brings multiple advantages into linguistics and computer science, especially in the development of AI systems and language-based software:

• Improved Communication: Semantics promotes better understanding across different languages, fostering clearer communication.

• Enhanced Machine Learning: In computer science, semantics helps machines to understand language in a way that's closer to how humans interpret and use language. This is fundamental to machine learning and artificial intelligence (AI).

• Contextual Understanding: Semantics deciphers the context behind phrases or sentences, facilitating accurate interpretation of an author’s intended meaning.

• Text Classification and Filtering: Semantic analysis is critical in categorizing, filtering, and understanding different types of content, thus enhancing applications like search engines, content aggregators, and web browsing software.

Despite its numerous advantages, Natural Language Semantics also comes with some challenges:

• Considerable Complexity: Semantics is a complex field that requires extensive knowledge and skills in linguistics and computer science.

• Ambiguity: Natural language is not formulaic, and words often have multiple meanings depending on the context, leading to potential ambiguity and misinterpretation.

• Cultural Differences: Semantics often depends on cultural and social norms, which can vary dramatically across different regions and groups, adding further complexity.

Overall, Natural Language Semantics is a fascinating and vital branch of linguistics and language processing. As technology continues to evolve and relies increasingly on understanding human language, Semantics will undoubtedly remain at the forefront of these developments. Its potential drawbacks can be mitigated through constant knowledge advancement, fostering an educated approach and careful planning.