Text & Natural Language Processing (NLP) Services

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are essential in orchestrating the development and enhancement of Text & Natural Language Processing (NLP) AI systems.

We handle strategic oversight, team coordination, and quality assurance, with a significant focus on training and onboarding workers to curate the data that powers these systems.

Training and Onboarding

PMs design and implement training programs to ensure data annotators grasp linguistic nuances, technical requirements, and project-specific goals. For instance, in bias detection, PMs might provide examples of biased vs. neutral phrasing, supported by case studies and interactive exercises. Onboarding includes practical annotation of sample texts, feedback loops, and alignment sessions to standardize worker outputs with AI needs. PMs also set up workflows, such as multi-tier reviews for complex tasks like multilingual translation.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., tagging 15,000 sentences for intent) and establish metrics like precision, recall, or inter-annotator agreement. They track progress through dashboards, resolve inefficiencies, and update guidelines based on worker insights or shifting client priorities.

Collaboration with AI Teams

PMs connect human annotators with machine learning engineers, converting technical specifications (e.g., model accuracy targets) into actionable tasks. They also manage schedules to sync data delivery with AI development timelines.

We Manage the Tasks Performed by Workers

The annotators, curators, or linguists perform the detailed work of preparing high-quality NLP datasets. Their efforts are precise and context-driven, requiring linguistic expertise and analytical skills.

Common tasks include:

Labeling and Tagging

For intent recognition, we might label a user query like “set an alarm” with the intent “schedule.” In topic modeling, they tag documents with themes like “technology” or “health.”

Contextual Analysis

For text summarization, our team distills articles into key points, ensuring meaning is retained. In bias detection, they evaluate phrases for fairness, considering cultural or social implications.

Flagging Violations

In text classification, our employees and subcontractors categorize content as “appropriate” or “inappropriate,” assigning labels like “offensive” or “safe” based on guidelines.

Edge Case Resolution

We address tricky cases—like ambiguous intents or idiomatic translations—often requiring team discussions or escalation to linguistic experts.

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

Data Volumes Needed to Improve AI

The volume of curated data required to train and refine NLP AI systems is substantial, reflecting the complexity of human language.

While specifics depend on task and model goals, general benchmarks include:

Baseline Training

A functional NLP model might need 10,000–50,000 labeled examples per category (e.g., 50,000 intent-labeled queries). For tasks like multilingual translation, this could rise to 100,000 to cover language pairs.

Iterative Refinement

To improve accuracy (e.g., from 80% to 90%), an additional 5,000–15,000 examples per error type (e.g., misclassified intents) are often required. For instance, refining paraphrasing might need 10,000 new variants.

Scale for Robustness

Large-scale NLP applications (e.g., global chatbots) demand datasets in the millions to handle diverse dialects, slang, or rare use cases. A conversational AI might start with 200,000 dialogues, growing by 50,000 annually.

Active Learning

Advanced systems leverage active learning, where AI flags uncertain cases for review. This reduces volume but requires ongoing annotation—perhaps 1,000–5,000 labels weekly—to sustain performance.

The scale necessitates distributed teams, often hundreds or thousands of workers worldwide, coordinated by PMs to ensure consistency and efficiency.

Multilingual & Multicultural Text & NLP Services

We can assist you with your text and NLP needs across diverse linguistic and cultural contexts.

Our team is equipped to navigate the subtleties of global languages, delivering precise and culturally attuned annotations tailored to your goals.

We work in the following languages: