Text annotation, big data, AI, and automation — you might have heard these terms come up often lately and for good reason. Natural language processing (NLP) is one of the most important areas of AI research and automation. With ChatGPT spawning a greater interest in NLP technologies, such as chatbots, automatic voice recognition, and sentiment analysis algorithms, boosting efficiency and productivity across several global industries. Currently, we are seeing advances in NLP demonstrate the ability to assist the users with differing speech abilities in freely communicating through automatic voice recognition equipment. However, developing complex products within platforms such as OpenAI requires text annotation tools — and with the magnitude of some datasets, automating text annotation is critical in delivering fast, accurate results.
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Large annotated text datasets are required to train NLP algorithms, and each project has different requirements. We've listed a brief overview of common types of text annotation for developers working on text datasets within the AI industry.
5 Types of Text Annotation for Data Sets
1. Entity Annotation
One of the most critical components in creating chatbot training datasets and other NLP training data is the use of entity annotation. Entity annotation identifies, extracts, and categorizes items in a data text. We explore various ways developers apply entity annotation below.
Named entity recognition (NER) is the process of annotating entities with proper names. Key tagging involves locating and labelling keywords or key phrases in text data.
POS Tagging recognizes and labels functional elements of speech, such as adjectives, nouns, adverbs, verbs, etc.
Entity annotation trains NLP models to recognize portions of speech, named entities, and keywords in a text. Annotators in this task extensively scan text, locate and highlight the target entities on the annotation platform, and select labels from a pre-populated list. We can often see the combination of entity annotation with entity linking to assist NLP models in learning more about named entities.
2. Entity Linking (EL)
Entity annotation involves locating and annotating specific entities inside a text. While entity linking involved a similar process, it is often used in extensive or large datasets. We can classify entity linking can into the following types:
End-to-end entity linking:
When identifying and extracting semantic text Entity Linking (EL) is a critical component in understanding large data sets. We can examine some standard methods that individually treat the Mention Detection (MD) and Entity Disambiguation (ED) stages of EL without taking advantage of their mutual dependency. End-to-end entity linking is the collaborative process of analyzing and annotating entities within a text (named entity recognition) and engaging in entity disambiguation.
Entity disambiguation matches identified entities to knowledge databases with information about them.
As user experience becomes a top priority in product development, entity linking can help improve search functions and overall UX. Annotators are responsible for associating labelled entities inside the text to a URL containing more information about the thing.
3. Text Classification
Text classification, or document classification, requires annotators to read a body of data text or small lines of text. Annotators must examine the material, determine the subject, intent, and sentiment, then categorize it using a specified list of categories.
What is the difference between Text Classification and Entity Annotation?
We can differentiate text classification as the act of classifying a whole body or line of text with a single label. In contrast, entity annotation, involves tagging of individual words or phrases.
Because text classification is such a broad area, many types of annotation, such as product categorization or sentiment annotation considered specialized forms of technical text classification.
Types of Text Classification
- Document classification: Document classification helps in sorting and recalling text-based content.
- Product classification: Product categorization, which is critical for eCommerce sites, involves the classification of products or services into intuitive classes and categories to increase search relevance and user experience. Annotators occasionally offer product descriptions, product photos, or both. The annotators would then select from a list of departments or categories provided by the customer.
- Sentiment annotation: Text classification based on emotion, opinion, or sentiment contained within the text. An example of sentiment annotation is when people on social media can "react" to posts with either positive, negative, or neutral emotions. While this works well for short sentences, larger datasets require sentiment annotation tools. We take a closer look at sentiment annotation below.
4. Sentiment Annotation
One of the most challenging areas of machine learning is emotional intelligence. Humans can have difficulty determining the genuine emotion behind a text message or email. A machine's ability to detect implications disguised in texts containing sarcasm, wit, or other casual forms of communication is exponentially more challenging. Humans train the models with sentiment-annotated text data to help machine learning models understand sentiment within data text.
Sentiment annotation, also known as sentiment analysis or opinion mining, is the tagging of emotion, opinion, or sentiment contained in a body of text. Annotators are given texts to study and must select the label that best represents the emotion or attitude expressed in the text. To put things in persepctive, a basic example would be the examination of client feedback. Annotators would read the reviews and assign ratings that range from positive, neutral, or negative
When created appropriately with accurate training data, a robust sentiment analysis model can detect the sentiment in user reviews, social media posts, and other sources. Therefore, the sentiment analysis methodology would allow firms to track public opinion about their products, formulating future strategies or modifying present ones as needed.
5. Linguistic annotation
Often referred to as corpus annotation, linguistic marks language data in text or audio recordings. Linguistic annotation requires annotators to detect and indicate grammatical, semantic, or phonetic components in text or audio data. While there are many types of linguistic annotations, we've listed common ones below.
- Discourse annotation: This annotation involves linking cataphors and anaphors to their antecedent or postcedent subjects.
"Marley went to the store. They purchased tools for work." <cite>Example of discourse annotation linking antecedent subject.</cite>
- Part-of-speech (POS) tagging: This type of tagging involves annotating the various function words within specified data text.
- Phonetic annotation: Phonetic annotation labels pauses, stress, and intonation in natural human speech.
- Semantic annotation: Semantic annotation involves tagging and applying metadata about concepts. Developers tag people, locations, companies, goods, or subjects within a text document or other unstructured content.
When we look at machine learning applications, linguistic annotation refers to annotated data in various formats, such as audio or text. Thus, data, including human-to-human communication via spoken or written text, is tagged with metadata and remarks to support ML.
What does this all mean? NLU, NLP, or other language-based AI model development, requires data annotation in audio, text, and video, as supporting information to make documents understandable.
Text Annotation Tools & Automated Workflows — A Wrk Case Study
There is an increasing demand for text annotation tools due to unstructured Big Data across social media, news, and compliance.
When using complex text annotation tools, the metadata required includes tags that highlight certain attributes. Specific key phrases, keywords, or sentences and require annotation tools to identigy proper names, sentiment, or intention using labels.
In short, text annotation appends notes to the text with different criteria based on your specific requirements and use case.
Deliver >94% Accuracy for Multi Level Classification with Wrk's Automated Text Annotation Tools
Text annotation tools have come a long way over the past decade. Up until recently, text annotation would still require manual human effort to verify complex text and label embedded information. When compiling data from several different sources, it can often lack structure and require data cleaning. Consequently, data cleaning, especially with robust datasets can be tedious and time-consuming. Add to that supervision and oversight, which can almost duplicate efforts.
At Wrk, we take a hybrid approach where we combine APIs, RPAs, and several other AI inputs with human intervention—through a skilled human community of workers.
The Text Annotation Wrkflow Request
Our client needed to find a scalable solution that wouldn't duplicate their team's efforts and their overhead. With our Wrkflow, we were able to help our client clean up their initial input files and output structured classified results with a high accuracy rate. <meta charset="utf-8"/>
Download this case study to see how we developed a Wrkflow to systematically deliver accurate annotation of complex text for multi-level, nested classification.
Check out the results in our Case Study today!
