A lightweight AI automatic annotation Excel plug-in

In the field of machine learning, data labeling is a crucial step, especially in natural language processing (NLP) tasks, where manually labeled data directly determines the training effect of the model. However, traditional manual labeling methods are not only inefficient, but also have problems such as high cost and strong subjectivity.

So, is there a way to   turn a common tool like  Excel into an AI data labeling assistant to improve labeling efficiency?

Challenges of Traditional Data Annotation

Traditional data annotation processes usually rely on manual processing of data one by one, which is cumbersome and inefficient, especially when dealing with large amounts of text. The following are common manual annotation tasks and their challenges:

• •  Sentiment classification : It requires manual reading of a large amount of user feedback and classifying the sentiments, such as positive, negative or neutral.
• •  Text summarization : extracting key information from a long article or report.
• •  Named Entity Recognition (NER) : Extract information such as person names, company names, places, etc. from text.
• •  Data cleaning : standardize irregular text data and remove noise.

The common problems of these tasks are:

• •  Slow speed : Manual labeling needs to be processed one by one, which takes a long time.
• •  Highly subjective : The results labeled by different people may differ, making it difficult to ensure consistency.
• •  High repetitiveness : Too many similar tasks can easily lead to fatigue and errors.

Let Excel participate in AI data annotation

In recent years, with  the development of large language model (LLM) technology, some plug-ins or extended functions have enabled Excel to have AI data annotation capabilities. For example, with AI plug-ins, users can directly enter formulas  in Excel  to automatically complete tasks such as text classification and entity recognition.

Case 1: Automatic labeling of sentiment classification

Scenario : Analyze user feedback and classify it according to sentiment (positive, negative, neutral).

Original data (Excel table) :

Excel AI formula example (Cell C2) :

=PROMPT(B2, "Please judge the sentiment of this user feedback and answer with 'positive', 'negative' or 'neutral'.")

Automatically generated annotation results :

✨Complete  batch annotation with one click, significantly improving efficiency!

Case 2: Named Entity Recognition (NER)

Scenario : Extract key information such as company name, person name, location, etc. from text.

Raw data :

Excel AI formula example (cell B2) :

=PROMPT(B2, "Please extract the company names from the text and list them separated by commas.")

Automatically generated annotation results :

Case 3: Email Classification

Problem : Need to analyze customer emails and extract key topics.

Raw data :

Excel AI formula example (cell B2) :

=PROMPT(A2, "Please summarize the subject of this email and categorize it as 'price consultation', 'performance issue' or 'after-sales service'.")

Automatically generated classification results :

Automatically classify emails to greatly improve customer support efficiency!

Advantages of AI Data Annotation

• •  Batch automation : quickly label thousands of data and significantly improve work efficiency.
• •  Easy to use : No need to write code, just use Excel formulas to complete complex tasks.
• •  Data security : Run AI models locally to ensure that sensitive data is not leaked.
• •  Low cost : Compared with manual labeling, it can significantly reduce data processing costs.

in conclusion

Excel combined with AI makes data annotation more efficient and accurate, greatly reducing the cost and time of manual annotation. Whether it is sentiment analysis, named entity recognition, or email classification, tasks can be completed automatically with the help of AI .