Sequence Tagging, a facet of Natural Language Processing (NLP) – the umbrella technology that enables machines to comprehend, respond to, and process human language – primarily involves classifying each entity or word in a given sequence or sentence according to a predefined set of labels. More explicitly, it serves to tag identifiers to each word or entity in a text string such that every piece of the sequence is associated with a meaningful, relevant category.

• Entity Classification: Every single token in the input sequence is mapped to a label, thus turning an unordered set of words into a meaningful arrangement carrying context.
• Structural Coherence: Label predictions across a sequence aren't made in isolation but by considering the entire sequence, thereby maintaining a certain structural coherence.
• Rich Annotation: The method helps enrich a text by annotating various parts of speech, chunking information, or recognizing named entities, among other things.
• Model Variations: Different models exist for sequence tagging, such as Conditional Random Fields (CRF), Hidden Markov Models (HMM), and Recurrent Neural Networks (RNN), each with its strengths and weaknesses.
• Practical Application: It is regularly used for tasks like Named Entity Recognition (NER), Part-of-Speech Tagging (POS), Chunking, etc., which find broad application in Sentiment Analysis, Information Retrieval, Text Summarization, etc.

Sequence tagging brings with it several advantages, as listed below:

• Information Extraction: It helps in extracting structured information from unstructured text, thereby enabling better comprehension and further processing of the data.
• Context Preservation: By considering the entire sequence while tagging, it preserves the overall context, which is critical for discerning the meaning and intent behind words.
• Multi-functional: It finds application in various NLP tasks, such as Named Entity Recognition, Part-of-Speech tagging, identifying sentiment, etc.
• Model Versatility: Sequence tagging encompasses different models, each with its strengths, thus offering a range of possibilities for application.
• Efficiency: It streamlines the process of extracting meaningful information from text data, enabling more efficient NLP tasks.

Despite its several benefits, sequence tagging in NLP has certain limitations:

• Machine Dependency: The output quality depends on the machine learning model's performance. If the model is not well-optimized or trained, the tagging could be inaccurate.
• Data Requirements: Sequence tagging requires a substantial amount of data to function effectively, which could pose a challenge in cases where large datasets are not available.
• Complexity: Like any machine learning task, sequence tagging can be complex and requires expertise to handle effectively.
• Speed Constraints: For longer sequences, the task can become computationally intensive and time-consuming.
• Requires Clear Labelling: If the label categories are not well-defined, the output may become ambiguous or misleading.

Implementing Sequence Tagging requires careful planning and consideration, starting with defining your need for information extraction, and the level of detail required. Following that, you select a suitable model for sequence tagging according to the specifics of your requirement, data availability, and computational resources. Once you've trained the model with your labelled data, evaluate its performance and further fine-tune it for optimized accuracy. The process cycles though these steps iteratively until you've achieved your desired tagging performance.