What is Text Annotation in Machine Learning and Types of Text Annotation
What is Text Annotation?
We use to interact with people around the world through different media such as text, audio, video, and images. While the most well-known approach to connect is through text. Different applications are utilized to convey through text.
The most popular applications where communication through text is popular are email, WhatsApp, Twitter, and customer service chatbots. Various ML techniques are used to instruct the machines on how to read, comprehend, analyze the data to produce the desired output.
As machines improve their capacity to decipher the human language, the significance of preparing and utilizing excellent text information turns out to be progressively unquestionable. This is the main reason why text annotation needs to be done more precisely and comprehensively to serve many industries.
Use of Text Annotation
Annotating text using ML techniques is the process of associating labels with the contents of the digital file like keywords, phrases, sentences, or sentiments. This is purely based on the NLP techniques where various sentence structures are highlighted by various text annotation methods.
Due to the intricacy of human language text annotation or labeling helps to prepare datasets for the training of ML and DL models for better utilization in various applications like the text to speech converters, syntax check applications, plagiarism check applications, auto Q/As, and automatic speech recognition. This can streamline the activities and transactions of various industries.
The prepared ML or DL models are utilized adequately to communicate in natural language and to complete monotonous jobs that humans feel exhausting. The prepared models save a ton of time and energy and do the targeted tasks precisely.
Types of Text Annotation
- Entity based Text Annotation
The term entity annotation is used for tagging various entities in a text. The method is used to prepare datasets for the training of ML and DL models. For the most part utilized for creating chatbot applications for finding, extracting, and labeling elements in the text. The entity annotation is subdivided into four categories;
- Name Entity Annotation
Annotating the name entities can feature the elements inside a text, like the names of people, organization, areas, and numerical values like the month or time and day of the week, and so forth. These entities are organized using various classification methods.
Extracting the principal entities in content helps to sort unstructured information and recognize significant data, which is vital on the off chance that you need to manage huge datasets. Check out Labellerr for the fastest text annotation.
- Keywords Annotation
Keywords Annotation is a methodology for locating key phrases or keywords in text. Also known as keyword extraction, this is frequently used to improve search-related functions for databases, e-commerce platforms, self-serve help sections of websites, and so on.
It is often used for digital content management, information retrieval, contextual advertisement, and recommender system. One of the most popular and publicly available keywords annotation databases is the OpenKP dataset containing 150,000 documents with the most relevant keywords and key phrases. A group of professional annotators on Labellerr’s marketplace for Data Labeling are providing large keyword annotation datasets for the training of ML and DL models.
- Part of Speech Annotation
The POS annotation is used to allocate a special tag to the words that are nouns, pronouns, adjectives, verbs, and so forth that indicate the parts of speech. This type of tagging is used for sentiment analysis, in the corpus searches and text analysis tools and algorithms.
The principal issue with POS labeling is vagueness. In English, numerous regular words have various implications and, in this manner, different POS. The job of a POS tagger is to determine this uncertainty precisely dependent on the context use. For instance, the “cheat” word can be used as a verb and a noun.
- Relation Extraction:
The relation extraction technique is used to find the relation between the entities and to identify the sentence structure. The technique is widely used in Q/As systems, Text Summarization, Grammar check tools, and creating knowledge graphs. To acquire large datasets of relation extraction for the training of ML and DL models Labeller will help you out.
- Text Classification
Text classification is the way toward annotating an entire body or line of text with a single label. The text is classified as categories and a unique tag is assigned to each category contextual data within lines or blocks of text.
Text classification is often used for topic labeling, detecting spam, product categorization, and sentiment analysis. Some of the use cases of text annotation are discussed further that are used for the training of ML and DL models
- Document Classification:
Document tagging is used for categorizing the content within a document using specified tags. The document classification is widely used by educational institutes and businesses for storing their different categorical data and also for their collaborative publishing, editing, and peer-review necessities.
Other use cases of document classification are locating sensitive information and detect documents with personal data. The company that provides a document classification facility is Labellerr. Various institutes using document classification are;
- Product Categorization
Product categorization is used for sorting the products based on the categories like price, color, size, and so forth. This is used by e-commerce businesses for better customer experience and for improving search relevance and product recommendations. Labellerr provides annotated datasets for high quality product categorization need. The most popular e-commerce websites that are using product categorization are;
- Sentiment Annotation:
Text annotation for sentiment analysis is used to train the ML and DL models for better classification of sentiments. Human beings exhibit different kinds of emotions through text. Recognizing these emotions is a challenging task in NLP, ML, and DL.
It’s difficult for a human being to spot or index the emotions like sarcasm, humor, arrogance, and many more from the text, and for this trained ML models are used. Sentiment analysis allows you to identify the customer response about the launch of a specific product or product experience.
It can also help to mitigate crises situation by taking immediate actions and protect the brand’s reputation. The most popular use case of sentiment annotation is the customer or buyer review, monitoring social media conversations, market research, banking, health care, government sectors, and call centers.
Sentiment analysis allows the brands to become more competitive, retain present customers, attract new customers, make customers more profitable, and improving market strategies. The top six sentiment analysis companies are;
- Entity Linking:
Entity linking is a process of allocating unique tags to the words of interest in a text. The process is used for linking the entities within a web content with other information databases for improving search-related functions and customer experience.
This principally includes connecting the tagged entities inside text information to a URL (uniform asset finder), which offers more data about the entity. Entity linking is mostly used in recommender systems, chatbots, and semantic search.
For example, typing the word “Health” on the search bar can show a number of websites associated with the word health. Entity linking is primarily based on two techniques; Content-based entity linking and Graph-based entity linking.
The most popular website that uses entity linking is Google that uses graph-based entity linking to enhance the efficiency of web-based search results and Wikipedia.org uses content-based entity linking where the data is always structured, accessible, and up to date.
Data – The Building Blocks of Text Annotation
According to the 2020 State of AI and Machine Learning report, 70% of companies reported that text is a type of data they use as part of their AI solutions.
Text annotation forms a very important part of NLP (Natural Language Processing) based models. It is done to develop communication mechanisms between humans communicating in their vernacular language.
Many companies choose to annotate their data manually or crowdsource them, these methods are neither reliable nor scalable. The ultimate goal of any data annotation project is to deliver efficiency while keeping your data safe and for this ML-based Data Annotation platforms like Labellerr are worth exploring.