Machine learning and artificial intelligence are tools in the modern technology toolbox, but they do not receive a lot of notoriety. So, you might be surprised to learn that over 70% of companies use text as their primary data for AI solutions—this, according to the 2020 State of AI and Machine learning report. There are many media types in the digital platform, such as text, audio, images, and video. Text is a common form of media preferred to communicate for personal and business purposes. Over the years, organizations have accumulated text data in an unstructured format. How can we use this text to our advantage?
Text annotation is the science—or perhaps the art—of adding information or metadata to define the characteristics of sentences, for example semantics or sentiments. It helps the machine distinguish or recognize words in a sentence, making it more sensible. This annotated text serves as a training dataset for AI and ML applications.
Why do we need to annotate text?
A precise training dataset gives the AI model the ability to learn and grow to interpret human language more consistently. Providing a complete set of training data at an early stage to machine learning algorithms can help develop self-predicting AI. In many cases, AI and ML developers prefer human annotators to highlight texts for various dialects, sentiments, meaning, and usage to maintain and improve accuracy.
Once the AI model starts learning the nuances of human language, it can label the keywords, phrases, or sentences. The main goal of text annotation is to assist the engine to understand human language.
Types of Text Annotation Techniques
Entity annotation teaches the AI model to recognize parts of speech, named entities, and key phrases in the text. Entity annotation is a vital task for Natural Language Understanding (NLU) workflow. Natural language understanding is a part of artificial intelligence that uses computer software to recognize the inputs in the form of sentences.
A single sentence has much metadata and information that can offer insightful information. For example, consider the phrase, “Mark lives in Ohio.” Mark is the Name, and Ohio is the Place. Once an AI model is presented with datasets of Name and Place, the AI model can actively label names and places in subsequent texts.
Some types of entity annotation are:
- Named entity recognition (NER) is the annotation of entities with proper names.
- Keyphrase tagging is the process of labeling keywords or keyphrases in text data.
- Part-of-speech (POS) tagging annotates functional elements of speech such as adjectives, nouns, adverbs, verbs, etc.
Entity linking is the process of annotating entities within the given text. It is often used to improve the user experience for search-related functions and involves the process of annotating entities to large repositories of data.
For example, “Paris is a beautiful city.” However, here Paris refers to a city and not a person’s name. Linking Paris to a more extensive database like Wikipedia gives information about the city.
Types of entity linking are:
- End-to-end entity linking is the combination of analyzing and annotating entities, followed by entity disambiguation.
- Entity disambiguation links named entities to a broader knowledge database such as Wikipedia.
Text classification annotates an entire line of text or content with a single label. The annotator reads the text, analyzes it based on the content, intent, or sentiment, and categorizes it based on the requirements of a predetermined tag.
A few common methods to train AI- and ML-enabled applications are:
- Document classification is used to sort and recall text-based content.
- Product categorization in e-commerce sites categorize products and improve the search experience.
- Sentiment annotation labels text based on emotions and sentiments.
One of the challenging tasks for AI and ML is to identify the human emotions in the text which are notoriously complicated to understand. Expecting a machine to understand these is unrealistic. Instead, machines are fed with sentiment-annotated text data to help them predict human emotions.
Another technique for adding sentiment involves the annotation of customer reviews. When a review is labeled as either positive, negative, or neutral, that helps the AI system to further learn about sentiment.
Consider the following examples of customer reviews:
The treadmill is good for small spaces. (Positive)
The quality of the toy is poor. (Negative)
Linguistic annotation is when you identify and label language data in text or audio, for example, grammatical, semantic, phonetic elements, and audio data. These annotated datasets are commonly used in chatbots, virtual assistants, search engines, machine translation, and more. Types of linguistic annotation include:
- Discourse annotation: Link the anaphors and cataphors to their antecedent subjects.
For example, Jessica had to work during the holidays. She felt sad about it.
- Part-of-speech (POS) tagging: label different function words within the text.
- Phonetic annotation: label intonation, stress, and natural pauses in speech.
- Semantic annotation: label word definitions.
Many organizations prefer human annotators to fulfill text annotation. The training datasets from the human annotators are generally considered to be more accurate and unbiased, making the AI model intelligent and enables further learning.
At PreludeSys, we offer impeccable text annotation service with access to cutting-edge technology and expertise. Our dedicated team is trained to provide customized text annotation as per your business and project requirements. We understand the struggle of handling unstructured texts which is why we devise a strategic text annotation plan that is highly efficient and cost-effective for your organization.