Text Data Cleaning & Normalization

This task polishes rough text—think “$50 usd” smoothed to “50.00 USD” or “teh” fixed to “the” (e.g., typos scrubbed, formats aligned)—to create clean, uniform datasets. Our team strips noise and standardizes entries, setting AI up for sharper accuracy and glitch-free processing in NLP workflows.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are pivotal in orchestrating the refinement and standardization of data for Text Data Cleaning & Normalization within NLP workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to transform raw text into pristine datasets that enhance AI’s performance and reliability.

Training and Onboarding

PMs design and implement training programs to ensure workers master cleaning techniques, normalization rules, and error detection. For example, they might train teams to fix “Jan 1st” to “01-01” or remove duplicate spaces, guided by sample texts and cleaning protocols. Onboarding includes hands-on tasks like correcting inconsistencies, feedback loops, and calibration sessions to align outputs with AI processing goals. PMs also establish workflows, such as multi-step validation for messy datasets.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., cleaning 20,000 text entries) and set metrics like error reduction, format consistency, or noise removal rate. They track progress via dashboards, address lingering issues, and refine methods based on worker insights or evolving normalization needs.

Collaboration with AI Teams

PMs connect cleaners with machine learning engineers, translating technical requirements (e.g., uniform encoding for NLP) into actionable cleaning tasks. They also manage timelines, ensuring normalized datasets align with AI training and deployment schedules.

We Manage the Tasks Performed by Workers

The cleaners, normalizers, or curators perform the detailed work of refining and structuring textual datasets for AI training. Their efforts are methodical and technical, requiring precision and attention to detail.

Labeling and Tagging

For cleaning, we might tag fixes as “corrected typo” or “standardized date.” In normalization tasks, they label entries like “uppercase unified” or “punctuation added.”

Contextual Analysis

Our team refines text, turning “10k” into “10000” or “n/a” into “not applicable,” ensuring AI trains on clear, consistent data across contexts.

Flagging Violations

Workers review datasets, flagging persistent errors (e.g., garbled text) or unfixable noise (e.g., corrupted strings), maintaining dataset quality and usability.

Edge Case Resolution

We tackle complex cases—like mixed encodings or regional formats—often requiring custom rules or escalation to data specialists.

We can quickly adapt to and operate within our clients’ NLP platforms, such as proprietary cleaning tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of items per shift, depending on the complexity of the text and cleaning required.

Data Volumes Needed to Improve AI

The volume of cleaned and normalized text data required to train and enhance AI systems varies based on the raw data’s messiness and the model’s complexity. General benchmarks provide a framework, tailored to specific needs:

Baseline Training

A functional NLP model might require 10,000–50,000 cleaned samples per category (e.g., 50,000 normalized reviews). For noisy or varied datasets, this could rise to ensure quality.

Iterative Refinement

To boost accuracy (e.g., from 85% to 95%), an additional 5,000–15,000 samples per issue (e.g., persistent typos) are often needed. For instance, refining a model might demand 10,000 new cleaned entries.

Scale for Robustness

Large-scale applications (e.g., enterprise NLP) require datasets in the hundreds of thousands to handle edge cases, format variations, or data shifts. A cleaning effort might start with 100,000 samples, expanding by 25,000 annually as systems scale.

Active Learning

Advanced systems use active learning, where AI flags problematic text for further cleaning. This reduces total volume but requires ongoing effort—perhaps 1,000–5,000 samples weekly—to sustain quality.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and clarity across datasets.

Multilingual & Multicultural Text Data Cleaning & Normalization

We can assist you with text data cleaning and normalization across diverse linguistic and cultural landscapes.

Our team is equipped to refine and standardize text data from global sources, ensuring clean, culturally relevant datasets tailored to your specific AI objectives.

We work in the following languages: