ML models and their subset Natural Language Processing (NLP) offer crucial advantages to companies in various industries. They help in analyzing text data, accelerating customer responses via chatbots, recognizing human emotions thanks to sentiment analysis, etc. The success of speech-related applications depends on correctly annotated text data.
What is text annotation?
Text annotation requires manual work. Data scientists determine the labels or “tags” and passes the text-specific information to the NLP model being trained. This process can be thought of as a child’s language learning process. Under the guidance of the parents who determine the labels, the child first learns the meaning of the words and then distinguishes the satire, metaphor, allusion, and emotion behind the sentence.
Why is text annotation important now?
Statista shows that the global NLP market generated turnovers of over $12 billion in 2020, and it is predicted that the market will grow at a compound annual growth rate (CAGR) of about 25% from 2021 to 2025, reaching revenues of over $43 billion. Since text annotation is the fundamental process in developing an NLP, it is reasonable to consider text annotation as an important phenomenon.
In addition, customers demand digitized and fast customer services, and the Covid-19 pandemic has increased this demand. Consequently, chatbots have become an integral part of customer service. No company would want to serve its customers with a weakly trained NLP algorithm that is not able to distinguish a simple metaphor.
What are the techniques for text annotation?
There are four main techniques of text annotation, namely:
Named entity recognition
Named entity recognition labels the words in the text with predefined categories such as date, name, location, etc. It is useful for machines to understand the topic of the text as AI learns keywords thanks to this labeling method. Therefore, named entity recognition is often used in the development of chatbots.
While entity annotation is about marking specific entities in a text, entity linking is about linking those entities to larger data sets such as Wikipedia links.
Sentiment annotation is the tagging of emotions and opinions contained in a text. Annotators choose which tag best represents the emotion of the document.
Understanding human emotions is crucial for companies to evaluate their position in the market. Sentiment annotation helps companies to improve customer satisfaction. Customer review analysis is an example of sentiment annotation, where data labelers read reviews and determine whether they are positive, neutral, or negative.
Here are the top sentiment analysis services on that market.
For effective chatbots in customer service, it is crucial to understand the reason for the conversation. Is the customer asking for something, reporting an unpleasant experience, waiting for a response or confirmation, etc.? Data analysts classify texts into different categories, such as request, command, or confirmation to train chatbots.
How to annotate text data?
Companies need software that specializes in text annotation to apply the text annotation techniques. It is possible to outsource the process to vendors that offer open-source and closed-source text annotation tools.
Open-source text annotation tools are free, and since the code is open to anyone, it can be modified to meet your organization’s needs. Closed-source tools, on the other hand, have a team to help you set up and use the software for your business. However they charge a fee for such a service.
Developing your own software for text annotation could be an alternative to outsourcing. However, this is a costly and slow process. The main advantage is that in-house tools provide greater data security.
In-housing vs outsourcing vs crowdsourcing
In-housing, outsourcing and crowdsourcing are ways to perform the manual work of text annotation. They are associated with different costs, output quality and data security. Therefore, it is an important strategic decision for companies which method to use.
Of course, the optimal strategy will vary from organization to organization, as the conditions and needs of organizations are different. Nevertheless, the following table might be helpful for you to choose the optimal strategy. For more, you can check our article on outsourcing data labeling.
|Quality of labeling
Don’t forget to check our sortable/filterable list of data labeling/annotation/classification vendors list. You can also check our open-source data labeling platforms list.
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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