Text annotation for machine learning – Short Explanation

Human language is involved, and the meaning of words changes based on context. With machines, merely providing the phrase alone is not enough. This is where data or text annotation for machine learning comes into the equation.

Text annotation for machine learning in the Real World

With text annotation, labels are applied to digital files and documents to highlight specific criteria better. To put this into context, consider how traditional translation software works.

With traditional software, a page is broken down into individual sentences and phrases. The sentences are then further shrunk to specific words, and each word is then translated based on its dictionary definition.

This method of translation leads to translation errors, both contextual and grammatical. When humans translate, they look not just at the words but also at the context of the sentence and how it applies to the whole page.

Text annotation for machine learning in the World of AI

With the importance of data to machine learning, text annotation plays a key role. Machine learning requires copious amounts of data for AI training, validation, and testing. In fact, according to the 2020 State of AI report, more than 70% of companies have indicated they use text with their AI solutions.

By using annotated data for training purposes, the algorithms learn what the expected results should be. After a sufficient period, the algorithms can then be tested on unannotated data to see if they can identify any similar patterns.

Four different methods are currently used for text annotation.

  1. Entity Annotation
    Regularly used as a method of training chatbots, entity annotation locates, and extracts text information. Named Entity Recognition (NER) annotates specific entities with proper names and is sometimes referred to as chunking. NER categories can include business names, individuals, locations, or more.
    Relation Extraction is a process for linking entities to understand the relationships between them better. Keyphrase Tagging looks for specific functional elements within the text, and Part-of-Speech (POS) Tagging is focused on finding the adverbs, nouns, and adjectives of a text sample.

  3. Text Classification
    A powerful tool for detecting spam or specific topics in a body of text, text classification annotates an entire body of text with a single label. Text classification includes Document Classification and Product Categorization.
    Document classification is primarily used in academic institutions as a way of contextually organizing resource materials. With product categorization, different products are sorted into unique categories. This is useful in eCommerce organizations.

  5. Sentiment Annotation
    Used when training ML algorithms, sentiment annotation improves the understanding of the meaning of a specific statement or phrase.

  7. Entity Linking
    Entity linking is most often used for improving user experience and search-related functions by annotating specific text entries within a body of the text.