High-quality information is gleaned from text data annotation. Learn how it’s used for machine learning to help improve the customer experience.
There’s no shortage of sources to be annotated for the purpose of teaching and training machine learning models.
E-commerce websites, customer surveys, emails, online reviews, social media accounts, chatbots, blog posts: these are all examples of content that we annotate to help these models acquire the ability to read, comprehend, and analyze text information.
In this article, we are going to discuss exactly what we mean by text annotation, different types of tasks that fall under this term, and how the text is annotated by humans for the purposes of machine learning.
What is text data annotation?
It’s a process where text data is tagged to help further developments in natural language processing (NLP).
The goal of natural language processing (NLP) is to create programs that can both recognize comprehensible human speech and communicate it back to produce easy, natural interaction with artificial intelligence (AI).
As is the case with all ML projects, the process is powered by data. In this case, that data is naturally formed written text (like a product review) that needs to be transcribed and annotated. As a result, the ML algorithm can make associations between real and expressed meanings to interact more naturally.
Tagged items include keywords, phrases, and sentences; sentiments; intention; proper names.
Types of Text Data Annotation
Sentiment analysis (or opinion mining) is a technique used to determine whether the text data has a positive, negative, or neutral connotation.
This data comes from social media monitoring, brand monitoring, customer support analysis, customer feedback analysis, and direct market research.
An example of sentiment annotation could be tweets directed at brands. Your company can compile all your mentions and a team of annotators goes in and tags them based on the opinion or attitude being expressed.
This gives you a sense of what people are saying about your products and services and trains the algorithm to detect these sentiments automatically.
Here, annotators evaluate the need or desire behind a text, classifying it into several categories – request, command, or confirmation.
Think, for example, of chatbot conversations. Users jump on and type things like “cancel my account,” “I want a refund,” “upgrade my services,” or “my order hasn’t arrived yet.” The AI can’t automatically recognize exactly what you need, much less why you need it.
The machine must learn what you’re asking for on top of recognizing whether you’re pleased or not with the product or service in question. This can be used to aid your customer service team by directing these inquiries to the correct entry on an FAQ page or to direct a customer to the correct department.
Metadata is added to pinpoint people, places, organizations, products, or topics. It’s kind of like adding notes in the margin of a book.
The annotation is represented as a set of tags that enrich the document, or specific fragments of it, with identifiers of concepts.
This type of annotation further indexes, classifies and links the identified concepts in a graph database. The whole point is to get to understand not only the words being expressed, but the concepts as well.
For example, semantic annotation takes the sentence “Aristotle, the author of Politics, established the Lyceum” and identifies Aristotle as a person and “Politics” as a written work of political philosophy, and the Lyceum as a hall for public lectures.
Entity annotation teaches NLP models how to identify parts of speech, named entities and keyphrases within a text.
It’s easier to explain by naming the different types of entity annotation:
- Named entity recognition (NER): The annotation of entities with proper names.
- Keyphrase tagging: The location and labeling of keywords or phrases in text data
- Part-of-speech (POS) tagging: The discernment and annotation of the functional elements of speech (i.e. adjectives, nouns, adverbs, verbs, etc.)
This task helps train the AI recognize not just what is being said, but what’s being discussed.
Here’s an example of NER to help illustrate via Telus International:
As you can see, people/band names are in orange, countries are lighter orange, cities are yellow, and album titles are red.
The annotator is tasked with identifying and flagging grammatical or phonetic elements in the text or audio data. Types of linguistic annotation include:
- Discourse annotation:The linking of anaphors and cataphors to their antecedent or postcedent subjects. Ex: James broke the chair. He felt bad about it.
- Phonetic annotation:The labeling of intonation, stress and natural pauses in speech
- Semantic annotation:The annotation of word definitions
Linguistic annotation is used to create AI training datasets for a variety of solutions like chatbots, virtual assistants, search engines and machine translation.
How is the text data annotated?
The human voice comes out in text as much as the spoken word, and in a world increasingly dominated by audio and video, text remains a valuable source of information to be mined.
Human involvement is required to keep NLP systems up to date, accurate and inclusive.
Just like transcription, humans are vital in the annotation process, and are especially valuable in analyzing sentiment.
So, how is it done? Trained annotators sift through the text and label according to your needs – it’s as simple as that.
Let’s use chatbots as an example. Start using one and it’s generally easy to tell how much of a human touch it possesses. For the AI to interact as naturally as possible, human annotators comb through chatbot text data and tag sentiment statements, intent directives, and so on.
So, if you type in “I have a problem with my streaming service and want a refund,” the chatbot will know exactly how to direct your inquiry, and even more so when you give your location, and it knows which region your service is located to serve you even better.
As to the how, there are large-scale text annotation and classification tools out there can help you achieve the deployment of your AI model quickly and more inexpensively. But again, those programs must first be taught to seek and tag what you need, and that begins with human annotation.
The best solution for your company depends on the complexity of the problem you’re trying to solve, as well as any cost and time restrictions.
If you want high-quality and comprehensive annotation, find a company that relies on the human eye and quality assurance nets to catch it all.
If you’re looking to keep costs down and get it done quickly, you can rely on an automated solution.
Feed Your AI with the Best Text Data Annotation
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