There are 3 main types of data annotation methods, namely image class, speech class, text class.
One, the image class
1, rectangular pull box
2D pull box, you need to pull a fit box, the box selected with the detection of the object (people, cars, plants, animals), the general box out of the box, but also need to play a corresponding label to mark the attributes (gender, age, color, size) and so on.
2, polygonal box
Polygonal box is slightly more difficult than the rectangular box, you need to outline the outline around the labeled elements, is in the form of a multi-point box, the same as the rectangular box, the polygonal box is the need to play the corresponding label to label the attributes.
3, OCR recognition
OCR has two methods of labeling, one is the use of multi-point box, the other is the need to box the contents of the absolute accuracy of the transcription, this labeling method is mainly used for text training more.
4, semantic segmentation
This kind of compared to pull the box to hit the point, relatively less, the need to distinguish between the elements of the picture, and each part of the labeling of the color filling respectively, the need to box part of the elements of the first keyed out with keying, and then select the corresponding attribute labels so that part of the elements are cut out.
5, pointing
Pointing is generally used for faces or key parts of the point marking, will be the location of the point of restriction and requirements, which will achieve high-precision detection and identification.
6, picture review and classification
The need to determine the picture, generally also divided into two kinds, one is the need to classify the picture, the other is to determine whether the picture is valid.
Two, speech class
1, speech transcription
Speech transcription is one of the most common speech annotation, the annotator needs to listen to a little speech and then transcribe the words they hear. Common languages include (Chinese, foreign languages, dialects), etc., according to the time can be divided into long or short speech, generally less than a minute (usually about three seconds) for short speech, which the length of the voice, the quality of the voice, with or without the results of the pre-labeling, whether the need to cut and other factors will have a greater impact on the difficulty of voice transcription.
2, other types of speech annotation
Other types of speech accounted for a relatively small proportion of a section of text and speech to determine whether the text and speech content corresponds to, or a section of speech annotation personnel to identify the voice to listen to the voice is not included in the illegal and sensitive elements.
Three, text class
1, emotional labeling
This labeling needs to be based on a sentence to determine the emotions contained in a sentence, generally (positive, neutral, negative) three levels, if the requirements are high, may be divided into six or even twelve levels of emotional labeling.
2, entity annotation
The need to extract the entities in a sentence, such as TV, refrigerator, basketball, and so on, and sometimes need to divide the sentence into categories such as wikipedia, music, news, or action instructions in the text.
3, similarity judgment
The need to determine whether the meaning of the two sentences are consistent. If consistent mark 1, inconsistent mark -1, can not be determined mark 0.
4, other types of text annotation
Other types of text annotation, such as public opinion annotation, to determine a paragraph of the article mentioned in the company is a positive or negative impact. There is also article sensitivity detection to determine whether the text content has illegal and sensitive information.
The role of data annotation
1, machine learning training: data annotation is a necessary step in training supervised machine learning models. By assigning labels or annotations to the data, the model can learn the relationship between the input data and the output labels to perform tasks such as classification, regression, and prediction. High-quality labeled data helps improve model performance.
2. Data analysis and insights: annotated data can be used for data analysis to help researchers and decision makers discover patterns, trends, and correlations in the data. This is critical for developing business strategies, market research and decision support.
3. Natural Language Processing: Text data annotation is used for natural language processing tasks such as sentiment analysis, named entity recognition, and machine translation. Labeling text helps to train text understanding models and improve the accuracy of text processing.
4, sound and speech processing: speech and audio data annotation is used for speech recognition, music classification, sound analysis and other applications. Labeling speech helps to train automatic speech recognition systems and audio processing tools.
5, medical diagnosis: medical image data annotation is crucial for medical diagnosis and treatment planning. By labeling X-ray, MRI and CT scan images, doctors can more accurately diagnose diseases.