Training Your Datasets To Understand Natural Language Intent
Our speaking language has transformed over the years, with words and phrases taking new meanings based on the context.
As humans, we understand the nuanced differences between such words and grab the meaning by gauging the speaker’s emotions.
But a machine learning program can have difficulties catching the expression and may even end up misunderstanding the whole sentence. That’s why text annotation is crucial.
The annotated data will train your ML models to understand the natural language of humans.
You can teach the machine to observe and sense what we (humans) mean through various datasets. Training your ML models with data combined with pre-processing is called Natural Language Processing (NLP).
How, why, and when is text annotation crucial?
Let’s consider a sentence fed into a machine: ‘You’re salty about missing out on drinks.’
Salty is a commonly used Gen-Z phrase that denotes resentfulness or bitterness.
But an ML model may interpret the term ‘salty’ literally — and connect it with ‘drinks’ and end up making wrong assumptions.
You can avoid such blunders and help your ML models learn and interpret human language correctly with proper text annotation.
How?
Text annotation uses a metatag to describe various attributes of the dataset.
By focusing on phrases, keywords, a dataset can be tagged with emotions like ‘happy,’ ‘angry,’ ‘irritated,’ or ‘sarcastic.’
If these tags aren’t correct, your ML models can misunderstand our language.
What happens when your ML models fail to understand the actual context?
- Your models make grammatical errors when communicating,
- Or keep asking questions to gain clarity,
- Or completely misunderstand the context.
It would be best to have comprehensive, properly-analyzed datasets to train your ML models to understand and communicate with humans effectively at scale.
Else.
Poor text annotation practices lead to inaccurate results and can complicate feeding more such information to the machine.
So, where is text annotation crucial?
Services like chatbots, voice assistants, search engines, translators, etc., are dependent on NLP-based ML models, and these models require accurate, contextual text annotation.
There’s another level of annotation complexity. Every NLP project has varying requirements for text annotations.
As a result, you must build suitable datasets and choose proper annotation techniques.
Five types of text annotations
1 — Entity Annotation
Entity annotation is crucial in chatbot training, especially for labeling unstructured sentences.
In entity annotation, you can train your ML models to identify:
- Keyphrases
- Location of keywords
- Differentiation of parts of speech
- Named entities
Through this process, your models will learn to read the entire phrase or sentence thoroughly, identify the keyphrases, understand the usage of the various parts of speech like nouns, verbs, adverbs, adjectives, etc.M/br>
2 — Sentiment Annotation
Many NLP models never work because they fail to understand sentiments. And therefore, sentiment annotation is by far the most-needed practice that needs refining.
While machines are great at acquiring knowledge and giving information, what it lacks the most is emotional intelligence. And this is where sentiment annotation is crucial.
Sentiment annotation is essential in analysing:
- Employee feedback,
- Customer engagement and interaction,
- Brand social listening,
- Virtual assistants,
- And customer reviews and comments.
You can train your ML models to identify emotions, opinions, and other such sentiments from the text through sentiment annotation.
How does this work?
Firstly, you must label a piece of text as positive, negative, or neutral.
As your ML model recognises these text labels, they begin to interpret the nature of the emotion and the meaning of the text.
3 — Intent Annotation
Intent annotation is crucial to interpret automatic AI call responses.
Why?
When the intent isn’t understood correctly, your ML models may keep asking for more information. Proper text annotation will help your ML models to analyse and sort the text into categories like confirmation, command, question, request, doubt, or answer. Using these categories, your models can interpret the text and provide a proper response consistently.
4 — Linguistic Annotation
Linguistic annotation is required to analyse the text in texts and audios.
It’s majorly used to identify phonetic and semantic elements, connect the parts of speech within the text, identify and link the pronouns to the previous sentence and understand the word definitions.
Here are a few subsections within linguistic annotation:
- Phonetic annotation.
- Discourse annotation.
- Semantic annotation.
- Parts of speech annotation.
Linguistic annotation adds tremendous value while building voice recognition models used in e-commerce stores and search engines, voice interaction chatbots, and translation models.
5 — Relationship Annotation
Relationship annotation is ideal for interpreting more than a piece of text.
As the name indicates, relationship annotation helps your ML models to link the relationships between various texts within a document.
This will help your ML models better understand the context of a text when it connects the text with the overall context of the rest of the document, rather than just considering a single sentence.
Who can train your datasets with natural language annotation?
Text annotators must be extremely thorough in building datasets that cover all the fundamentals needed to teach your ML models.
Based on the application and complexity of the project, you may need to drill deep to identify suitable annotation types that boost your models’ accuracy.
Traindata has data experts with over 15 years of experience labeling and annotating data for ML projects.
We are experts in human-in-the-loop text annotation, image annotation, video annotation, and audio annotation for academic or business cases.
Please email us at karthikv@train-data.com to discuss your data annotation needs.
P.S: This blog post appeared originally on Traindata’s blog. To read more insights on data labeling, visit Traindata.us/blog
