Different Types Of Text Annotation For Machine Learning

Machine Learning is one of the most talked-about concepts. It is a part of AI that works on the analysis of data and interpreting it in a way that the response arisen from it is accurate and human-like. Well, for the Machine Learning algorithm to work properly and assess the data well, the data need to be labeled in a way that is easily and accurately understandable by the Machine Learning algorithm. In this blog, we will be unfolding popular types of annotation for machine learning.

What is an annotation?

Before heading further, let's have a recap at what annotation is. For any data to become comprehendible by the machine learning system, it is improved that the data is prepared in a  manner that the system can easily find the pattern and inferences from them. This is done by adding metadata to the dataset. Any metadata tag which is used to mark the data is known as an annotation. For the machine learning system to understand it more accurately, it is important that this marking of data is done more accurately. 

Types of annotation for machine learning :

1. Phrase chunking- It consists of tagging parts of speech along with their grammatical meaning.

2. Semantic annotation- In these various concepts are annotated within texts like people, company names, objects. Machine Learning makes use of this kind of annotation to categorize new concepts. It helps in improving search relevance and preparing chatbots to answer more aptly.

3. Entity annotation-  It is used for labeling unstructured sentences with the right information, which can be easily comprehended by machine. There are different processes to come together to make the language easy to understand.

4. Image and video annotation- This method is used to train the machine learning system to analyze and block sensitive content,  e-commerce product listing, and guiding autonomous vehicles. With this annotation, it becomes easier for a machine learning system to understand images and videos.

Machine Learning System In Natural Language Annotation

Our text analysis functions are based on patterns and rules. Each time we add a new language, we begin by coding in the patterns and rules that the language follows. Then our supervised and unsupervised machine learning models keep those rules in mind when developing their classifiers. We apply variations on this system for low-, mid-, and high-level text functions.

Low-level text functions are the initial processes through which you run any text input. These functions are the first step in turning unstructured text into structured data; thus, these low-level functions form the base layer of information from which our mid-level functions draw on. Mid-level text analytics functions involve extracting the important content of a document of text. This means who is speaking, what they are saying, and what they are talking about.