Your AI innovation will only be as good as the data used to train it. Here’s 3 key elements of data fixing to consider.
We’ve spent some time lately talking up our specialized Natural Language Processing services. In this article, we want to focus again on data, and fixing in particular.
You put tremendous resources and effort behind ensuring the quality of your data. Quality issues can arise at any time from any number of directions, though.
And it can come about at any point in the process – from collection to immediately after we certify that the data is clean.
Here’s a few areas where data fixing can come in super handy.
Human Assisted Synthetic Data Creation
Sometimes, it doesn’t make sense to scrape data. It may be faster and cheaper to curate a synthetic dataset.
Therefore, a one-size-fits-all synthetic approach to the unique world of speech transcription is destined for failure.
Projects that require synthetic data will be analyzed by our Solution Architects, project management team, and lead linguists. Afterwards, they’ll create synthetic data that has the human touch it requires.
Policy Compliance Redaction
If you have training data but you can’t use it because it contains sensitive or personal information, we can handle this though one of the following:
- Classification: labeling according to type and sensitivity
- Generalization: characterizing the data to hide private information
- Swapping: rearranging the data by exchanging values
- Suppression: deleting or removing pieces of information
So, for example, we can pseudonymize the data according to your rules.
Pseudonymization is a data management and de-identification procedure. We can replace personally identifiable information fields within a data record with one or more artificial identifiers, or pseudonyms.
A single pseudonym for each field or collection of replaced fields makes the data record less identifiable while remaining suitable for data analysis and data processing.
For data redaction projects, we don’t rely on Named Entity Recognition and dictionaries. Rather, we lean on human intelligence. The most advanced NLP systems still need human touchpoints.
Customer engagement is important any public business and inappropriate posts could damage your brand’s reputation. That includes Google reviews or social media replies, for example.
With content moderation and sentiment analysis, we identify potentially risky content.
On a base level, sentiment analysis determines whether data is positive, negative, or neutral. NLP and machine learning algorithms make sense of data through text classification.
Sentiment analysis can also move beyond positive, negative, or neutral to offer more specific feelings.
It can cover a wide spectrum, as well as detect more specific feelings and even intentions. The level of information you receive is dependent on your specific needs, and the output is tailored accordingly.
It helps businesses monitor their brand health and parse large amounts of customer feedback to better understand customer needs.
It offers the opportunity to protect your brand, suppressing sparks before they become multi-alarm fires.
Need Data Fixing? Partner With Us
As innovators in the data collection and annotation space, Summa Linguae Technologies offers flexible, customizable data services that evolve with your needs.
See how we can help you with your data annotation project.
Learn more about our data solutions and contact us today.
And in case you missed the previous entries in this series:
Speaking Your Customers’ Language: How Multilingual Text Data Empowers Cha...
Multilingual text data forms the cornerstone of training chatbots to operate effectively across language b...
The Impact of Accurate Data Labeling on Model Performance
Discover how accurate data labeling transforms the chaos of raw data into clarity, significantly impacting...