We interact with different media (such as text, audio, images, and video) every day, and our brains process and process the information collected to guide our behavior. Text is one of the most common types of media in the information we encounter on a daily basis, made up of the language we communicate with. Artificial intelligence, through machine learning (Machine Learning), learns how to read, understand, analyze and generate text in a valuable way, so as to realize the technical interaction with human beings and create value. According to the State of AI and Machine Learning 2022 report, 70% of companies report that text data processing is part of their AI solutions. This makes sense, because the intelligent processing of textual information will result in substantial cost savings and more revenue for all industries. However, as a part of language, text has many dimensional features in addition to the logical and clear levels such as basic word meaning, attributes, and grammar: context, emotion, purpose, and so on. If artificial intelligence cannot understand these complex contents, it must not be able to understand human language correctly. Therefore, we need to use higher-quality text data for machine training to cultivate artificial intelligence that can correctly understand text. Like other training data, we need comprehensive and accurate text annotations to create such text data. This article will introduce the concept, application, classification, method of text annotation in detail, and how to choose the annotation method that suits you.
What are text annotations?
Text annotation is the process of characterizing text. In this process, we clarify the multi-dimensional features of the text, and label it with specific semantics, composition, context, purpose, emotion and other metadata to create a huge text data set (text training data ) . By annotating well-labeled training data, we can teach machines how to recognize human intentions or emotions hidden in text, and understand language more "humanly". It should be noted that we need to use very comprehensive and accurate high-quality text data in order to cultivate a "smart" artificial intelligence. Text annotations, if not handled properly, can lead to machines not being able to correctly understand the text content, such as showing grammatical errors, causing clarity or context issues. If you ask your bank's chatbot, "How do I suspend my account?" and it replies, "Your account is not suspended," it's clear that the machine misunderstood the question and needs to be rewritten with more accurately labeled data. train.
Application of Text Data Labeling
By learning accurately labeled text data, machines will be able to use natural language to communicate effectively enough, analyze text data in multiple dimensions, and replace humans to do some repetitive and monotonous tasks, thus freeing up time, money, and resources for organizations to focus on for more strategic work. The applications of natural language-based AI systems are endless: smart chatbots, improved e-commerce experiences, voice assistants, machine translators, more efficient search engines, and more. The ability to simplify transactions by leveraging high-quality textual data has a profound impact on customer experience and business bottom line across every industry.
Types of textual data annotations
Text annotations include various types, such as sentiment, intent, semantics, and relation. These options apply to many human languages. The following are the main types of text annotation labels:
Text Sentiment Labeling
Sentiment labeling evaluates the underlying attitudes and sentiments in text, labeling text as positive, negative, or neutral, etc.
Text Intent Annotation
Intent annotation analyzes implicit needs or desires in text, grouping them into categories such as request, command, or confirmation.
Text Semantic Annotation
Semantic annotation identifies and labels the meaning of concepts and entities (such as people, places, or themes) referenced in text.
Text relation annotation
Relational annotation aims to discern various relationships between parts of a document; typical tasks include dependency resolution and reference resolution.
Ways to meet the needs of text annotation
There are four main ways to meet the needs of text data labeling. We can evaluate and select according to the specific conditions of enterprises and institutions, and use multiple ways in combination. 1. Human Annotation Most organizations seek human annotators to annotate text data because in text analytics, human annotators can discern subtle emotional nuances and understand usage trends in slang, dialect, and other language usage. We can find suitable human annotators by using our own employees, finding freelancers, and asking for help from crowdsourcing platforms. 2. Labeling tools At the same time, there are many text labeling tools and text labeling systems on the market, which can also help you quickly deploy artificial intelligence models at a lower cost. These tools can help you with text data pre-classification and other tasks, but text annotation should always use "human-machine collaboration" to ensure quality. 3. Datasets At the same time, if the customization requirements for text training are low, we can also choose labeled text datasets for machine training. These include some open source datasets, and some more professional paid datasets. Appen has a huge language dataset, including Mandarin Chinese and multiple dialects, and more than 200 languages from all over the world. 4. Outsourced labeling services In the case of relatively professional needs, a large amount of data, short-term needs, or the company itself does not have relevant knowledge and existing resources, etc., you can choose the services of text labeling experts . Many text annotation platforms and service providers have rich experience, linguistic experts, machine training experts, and the ability to quickly gather many human annotation staff to meet the demand efficiently with quality and quantity, and ensure the progress of artificial intelligence deployment. The specific annotation method used depends on the complexity of the problem you are trying to solve, and the amount of resources you can devote to it. Below we will share Appen’s experience in assessing the needs of text data labeling.
How do enterprises and institutions choose the appropriate text annotation method?
Appen relies on its own team of experts to provide annotated data suitable for client machine learning tools. Yao Xu, one of our product managers, will help ensure that Appen Data Annotation Platform exceeds industry standards in delivering high-quality text annotation services. With an academic background in science and linguistics, she is trilingual and has researched extensively in machine learning and Natural Language Processing. Key points she makes when evaluating and addressing your text annotation needs include:
What kind of data is needed
Determine the type of annotation you need for your model's training data—whether it's document-level annotation or cloze, whether you're collecting data from scratch, labeling it, or looking at machine predictions. Defining your goals is a crucial first step.
How much data is needed and how often
The amount of data and the data required are important factors in determining the data labeling strategy. When your needs are low, start with an open source annotation tool or subscribe to a self-service platform. However, if you foresee a rapidly growing need for annotated text data in your team, take the time to evaluate your options and choose a platform or service partner that will work in the long run.
Whether the data belongs to a specialized domain or contains multiple language dialects
Text data in specialized domains or containing multiple language dialects may require annotators to have relevant knowledge and skills. This can be a limiting factor as you expand your text data labeling efforts. In this case, it is imperative to choose a suitable partner who can meet these special needs.
what resources do you have
You may have an experienced engineering team working on your data and building models, you may already have a team of expert annotators, and you may even have your own annotation tools. No matter what resources you have, you want to get the most out of your resources while acquiring external resources.
Go beyond text-based data
Text data can also be extracted from image, audio and video files. If such a need arises, you need an annotation platform or data service provider capable of handling the transcription tasks from these non-text data. This should also be considered when choosing a labeling solution.