Named Entity Recognition (NER), a subarea of information extraction, is an integral part of Natural Language Processing (NLP). It involves the identification of predefined classes of real-world entities such as organizations, persons, locations, and expressions of times present in a text. Similar to the COTS software, NER also exhibits certain key features:

• Wide implementation: As the importance of understanding and sorting information grows, NER is actively implemented in various domains like search engines, customer service, chatbots, and data analysis.
• Standard Format: NER uses standard formats to categorize entities, enabling the comprehension of semantical nuances in a text.
• Minimum manual intervention: Though manual checks can ensure precision, NER typically operates with minimum human intervention.
• Availability: Numerous NER tools are readily available, saving the time and effort of developing an in-house tool.

An effective approach to implementing NER includes in-depth identification of the entities required to be recognized. The next step involves identifying the right model or library that suits your requirements. You should have a robust evaluation system to keep a check on the model's performance. Also, constant updating and retraining of the model should be part of the maintenance procedure. Proper planning, evaluation, and constant improvements are the stepping stones for a successful NER implementation.

Undoubtedly, Named Entity Recognition in Text has revolutionized the way we extract and analyze information. By bringing down the complexity, time, and cost involved in data extraction and analysis, NER has become an indispensable tool in the era of data-driven decision making. While it has a few limitations, careful planning and strategic adaption can help in making the most out of this powerful method.

Adopting NER offers several distinct benefits, some of which include:

• Reduced time: With the help of NER, the extraction of vital entities from large chunks of text can be done in record time, which otherwise would be a lengthy and error-prone task if done manually.
• Custom solutions: Tools such as Stanford's Named Entity Recognizer and the Spacy library of Python language can be customized according to the specific needs of a project.
• Dependable results: The use of established libraries guarantees reliable results as they undergo frequent testing and updates to eliminate bugs and enhance performance.
• Reduces the complexity of comprehension: By identifying the critical entities, NER simplifies the understanding of the text.
• Enhanced decision making: The ability to extract key entities enables machine learning algorithms to make informed decisions.

However, like any other tool, it's important to consider the potential drawbacks before implementing NER:

• Limited scope: NER's functionality is limited to recognizing and categorizing entities. It does not offer an understanding of the overall semantics of a text or the intricate relationships between the recognized entities.
• Language dependency: Most of the advanced NER tools work effectively on English texts. However, their performance drops significantly when applied on texts in other languages.
• Training data requirement: For optimum performance, NER models need to be trained thoroughly on domain-specific data. This can be a time-consuming and resource-intensive process.
• Homonym handling: NER can stumble upon homonyms - words with multiple meanings - thus leading to incorrect entity identification.