Data annotation is a critical task you need to get your innovation off the ground. Here’s three services you need to know.
Artificial Intelligence doesn’t emerge out of nowhere. It requires a huge amount of data to develop, and that data’s only useful after you effectively analyzed and annotated it.
And there’s no shortage of data to annotate for the purpose of teaching and training the relevant machine learning models.
Let’s discuss a few types of data annotation services in particular, and how our specialized experts can help you.
Text Data Annotation Services
Entity annotation, sentiment analysis, and linguistic tagging are our most common text annotation tasks we do.
So, what are they?
Entity annotation teaches natural language processing models how to identify specific parts of speech like named entities and key phrases within a text.
This task helps train the AI to recognize not just what people say, but the subject of the discussion.
It’s easier to explain by naming the different types of entity annotation:
- Named Entity Recognition (NER): The annotation of entities with proper names
- Keyphrase tagging: The location and labeling of keywords or phrases in text data
- Part-of-speech (POS) tagging: The discernment and annotation of the functional elements of speech (i.e., adjectives, nouns, adverbs, verbs, etc.)
Sentiment analysis is also known as opinion mining. This task determines whether the text data has a positive, negative, or neutral connotation.
The data comes from social media monitoring, brand monitoring, customer support analysis, customer feedback analysis, and direct market research.
Google reviews is a source for sentiment annotation. Your company compiles all your feedback and a team of annotators tags them based on the opinions or attitudes expressed.
In linguistic tagging, the annotator identifies and flags grammatical or phonetic elements in the data. Types of linguistic annotation include:
- Discourse annotation: Linking anaphors and cataphors to their antecedent or postcedent subjects. Ex: “James broke the chair. He felt bad about it.”
- Phonetic annotation: Labeling of intonation, stress, and natural pauses in speech
- Semantic annotation: Annotation of word definitions
So, linguistic annotation creates AI training datasets for a variety of solutions like chatbots, virtual assistants, search engines and machine translation.
Read more about how to annotate text data here.
Image Annotation
Reliable human annotation on image data improves your computer vision and pattern recognition systems.
In image classification, the computer is taught to identify an object that resembles something known from previously labeled images.
For example, after labeling images of different animals and detecting their presence, the machine is now taught to recognize monkeys, elephants, and so on.
There’s also semantic segmentation. Here, machine learning starts to familiarize itself beyond the target object.
This image annotation technique is a pixel-wise annotation, where every pixel has its own label. Pixel-wise annotation plays a significant role in self-driving cars, for example, where annotating images of their surroundings is crucial.
3D cuboids measure distances between two points or landmarks for moving objects, and lines and splines for road signs detection.
AI annotation is the way of the future. However, humans must still check it in order to get the best possible and most accurate readings of your image data..
Audio Transcription and Annotation
Time stamping and meta tagging help your systems learn what to ignore.
The annotation process consists of labeling noises, repetitions, false starts, changes in language, and who is speaking.
We support both human annotation from scratch and human review of ASR output.
We find the following speech annotation and transcription process:
- Annotation – One annotator works on segmentation, speaker tagging, and meta data
- Partial QA – One of our team members QAs a sampling of the annotated files to ensure they’re ready for transcription
- Transcription – A different transcriber inserts the transcription and any necessary tags
- Full QA – The same QA reviews 100% of the transcription files
By following this multi-step process – beginning with annotating the speech data and then performing partial QA – we ensure that the transcription step is as efficient as possible.
We err on the side of human annotation and transcription to ensure accuracy and inclusivity, and to handle complex environments and use cases.
Get to Know our Data Annotation Services
As innovators in the data collection space, we offer flexible, customizable data services that evolve with your needs.
Render your data meaningful and train your algorithm free from biases with our labeling and classification services for text, speech, image, and video data.
So, contact us today to learn more.
And in case you missed the previous entries in this series:
