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How do you document NLP workflows?

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Natural language processing (NLP) is a branch of artificial intelligence (AI) that deals with the interaction between human language and computers. NLP workflows are the steps and tools involved in creating, testing, and deploying NLP applications, such as chatbots, sentiment analysis, or text summarization. Documenting NLP workflows is essential for ensuring quality, reproducibility, and collaboration in NLP projects. In this article, you will learn how to document NLP workflows using some best practices and examples.

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Dr Djamila Amimer

Helping Businesses Unlock AI Potential | CEO & Founder | Top 10 Global Thought Leaders on AI, Predictive Analysis and…
Sugandha Sahay

Award-winning Technology Strategy Leader | Helping program managers to apply hyperautomation in business operations
Aldo Segnini

AI-Powered Digital Transformation Strategist | Empowering Executives with Data-Driven Insights | +25 Years of Proven…

1 Define the problem

The first step in documenting NLP workflows is to define the problem that you are trying to solve with NLP. This includes identifying the input and output data, the target audience and use case, the performance metrics and evaluation criteria, and the ethical and legal implications of your solution. You should also provide a clear and concise summary of your problem statement and objectives in your documentation.

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Sugandha Sahay

Award-winning Technology Strategy Leader | Helping program managers to apply hyperautomation in business operations
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One additional item to include in the first step is non-goals i.e. what we won't include in the MVP. While we work hard defining the problem, use cases, target audience, we often forget where do we draw the line for the MVP. In my experience, documenting non-goals early on becomes a key prioritization input during event of scope creep.

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2 Choose the methods

The next step is to choose the methods and techniques that you will use to implement your NLP solution. This may involve selecting the appropriate NLP tasks, such as tokenization, stemming, lemmatization, part-of-speech tagging, named entity recognition, sentiment analysis, etc. You should also decide on the type and source of data that you will use, such as text, speech, or images, and whether you will use pre-trained models, custom models, or a combination of the two. Document the rationale and assumptions behind your choices, as well as the advantages and limitations of each method.

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Aldo Segnini

AI-Powered Digital Transformation Strategist | Empowering Executives with Data-Driven Insights | +25 Years of Proven Success in Implementing Tech Solutions | Startup Advisor | EaaS
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When documenting NLP workflows, it's crucial to outline the methods and techniques you plan to use to implement your NLP solution. This includes selecting the appropriate NLP tasks like tokenization, stemming, part-of-speech tagging, and sentiment analysis. Additionally, you should specify the type and source of data you'll be working with, whether it's text, speech, or images, and clarify whether you'll be utilizing pre-trained models, custom models, or a combination of both. Make sure to document the reasoning behind your choices and highlight the advantages and limitations of each method to ensure clarity and reproducibility in your NLP project.

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3 Design the architecture

Next, design the architecture of your NLP solution, which is the structure and components of your system. This may include the data pipeline, the model architecture, the user interface, and the deployment environment. Document the architecture using diagrams, flowcharts, or pseudocode, and explain how each component works and interacts with others. You should also document the dependencies, requirements, and specifications of your system, such as the libraries, frameworks, tools, and hardware that you will use.

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4 Implement the code

The fourth step is to implement the code for your NLP solution, which is the actual programming and coding of your system. Document the code using comments, docstrings, and annotations, and follow the coding standards and conventions of your language and framework. You should also document the testing and debugging processes, such as the unit tests, integration tests, and error handling methods that you will use to ensure the quality and functionality of your code.

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5 Evaluate the results

Evaluate the results of your NLP solution, which is the assessment and analysis of your system's performance and outcomes. Document the results using tables, charts, graphs, or reports, and compare them with the predefined metrics and criteria that you set in the first step. You should also document the limitations, challenges, and errors that you encountered, as well as the possible improvements and extensions that you can make to your solution.

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6 Share the insights

Finally, share the insights that you gained from your NLP solution, which is the communication and dissemination of your findings and learnings to your stakeholders and peers. Document the insights using summaries, presentations, or articles, and highlight the key takeaways, implications, and recommendations that you derived from your solution. You should also note the feedback, comments, and suggestions that you received from your audience, and how you plan to incorporate them into your future work.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Dr Djamila Amimer

Helping Businesses Unlock AI Potential | CEO & Founder | Top 10 Global Thought Leaders on AI, Predictive Analysis and Emerging Tech. | AI Speaker | AI Author| LinkedIn Top AI Voice
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Worth mentioning NLP workflows for our readers. Standard NLP workflow includes: - Text pre-processing - Exploratory analysis and text parcing - Feature engineering - NLP modelling - NLP evaluation

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Ira Whiteside

Information Architect
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In addition, another point would be added to Dr. DJamila Excellent suggestions regarding Unemp wi. With the advent of LLM and Chat LLM. LLlM Acquire knowledge of chain of the thought, decision trees, and forest of thought there are many things in AI and LLM and BI That are that’s also termed AI, as well as NLP, that require different roles and skills again analogous to BI that have to be considered

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How do you document NLP workflows?

1

2

3

4

5

6

7

1 Define the problem

2 Choose the methods

3 Design the architecture

4 Implement the code

5 Evaluate the results

6 Share the insights

7 Here’s what else to consider

Artificial Intelligence

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How do you document NLP workflows?

1

2

3

4

5

6

7

1 Define the problem

2 Choose the methods

3 Design the architecture

4 Implement the code

5 Evaluate the results

6 Share the insights

7 Here’s what else to consider

Artificial Intelligence

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