Simply put, text annotation in machine learning ( ML ) is the process of associating labels to a digital file or document and its content. This is an NLP method where different types of sentence structures are highlighted by various criteria. Because human language is quite complex, annotation helps prepare datasets that can be used to train ML and DL models for a variety of applications.
These include various NLP technologies like neural machine translation ( NMT ) programs, auto Q&A ( question and answer ) platforms, smart chatbots, sentiment analysis, text-to-speech ( TTS ) synthesizers and auto speech recognition ( ASR ) tools, among other related projects. These technologies can streamline the activities and transactions of many organizations across different industries.
Why do we need to annotate text?
Before the nascent rise of tools that use machine learning and deep learning models for efficiently overcoming these challenges, traditional software was designed to perform phrase-based processing. This is where the software breaks down blocks of text into sentences, which is in turn broken down further into phrases. Afterwards, these phrases are automatically converted into the target output, based on a set of pre-defined rules.
For example, traditional translation programs are often designed to process an input set of sentences or paragraphs. It’s hand-engineered to break this input text into smaller chunks of phrases as a pre-processing step. It then converts those phrases to its translations in the target language, based on a large set of rigid hand-engineered rules. The software then combines those translated chunks to represent a translated version of the input block of text.
As you can posit from that traditional process, this usually results to contextual clarity issues, erroneous grammatical problems, and unnatural-sounding translations of sentences and paragraphs. That’s mainly because this is not how human translations are performed. The natural process is to fully understand the context of an entire sentence or a full paragraph, before translating it from a source language to a target language, all while keeping its contextual meanings intact and simultaneously following grammatical rules of the target language.
Annotating Text Datasets for Training Machine Learning Models
Nowadays the new paradigm is machine learning. Datasets with large corpuses of annotated text are frequently used to train neural networks for these applications.
Text annotation is used to prepare a dataset for training machine learning models of an NMT program. These tools normally use a sequence-to-sequence (seq2seq) neural network. This is to fulfill its intended functions for automatically translating a block of text from one source language to the target language. For example, these models can be trained to automatically translate a sentence in Mandarin Chinese to American English.
Today, a modern-day translation tool makes use of recurrent neural networks (RNNs) as its seq2seq model for performing its translation functions. It often has an encoder that’s designed to convert a sentence or a paragraph into a sequence of numbers, which is commonly called a thought vector or a meaning vector. Meanwhile, it also has a decoder that processes these sequences of numbers and converts it to a translated block of text in the target language. This is also known as an encoder-decoder architecture, which efficiently mimicks how natural translations are performed by humans.
A dataset with annotated text translation pairs are converted into tensors during training. This is performed by tools like Tensorflow, Keras and others. That’s because tensor inputs containing word indices are required to train an NMT tool’s RNN encoder and decoder. These tensors are in time-major format and include source text inputs, target text inputs and outputs.
When choosing or developing a dataset for training a translation tool’s models, don’t forget that an individual vocabulary set with size V for its source and target languages is needed to make these models work properly. For each language, embedding weights are then generated from supervised and unsupervised learning during training. Meanwhile, text that isn’t part of the most commonly used words in these vocabulary sets isn’t given unique embeddings. Instead, they’re supplied with the same embedding as an “unknown” token.
As mentioned, training such models requires a dataset with annotated text translation pairs. However, keep in mind that multiple sentence input batches should be provided to the main network simultaneously, as this is known to improve model efficiency.
But training ML and DL models for similar NLP applications require huge datasets with text content. And, each requirement has various prerequisites. So for those who need to build text datasets, here’s an introduction to 4 different types of text annotation methods:
What are the types of Text Annotation?
Datasets with text annotations usually contain highlighted or underlined key pieces of text, along with notes around its margins. By annotating text, you can ensure that the target reader, in this case a computer, can better understand key elements of the data.
The process of annotating text involves any action that deliberately interacts with digital contextual data. Remember, this is to enhance the reader’s understanding of the text. So when annotating your datasets, you should primarily focus on key areas that can make it quicker and easier for a human reader or a machine to understand, index and store the text in a database for later use.
4 of the Most Widely Used Text Annotation Methods
- Entity Annotation
Entity annotation is a procedure to generate training datasets for your ML and DL models. Mostly used for developing chatbot applications, this is the process of locating, extracting and tagging entities in text. Here are some text annotation example subsets:
- Named Entity Recognition ( NER )
This is a method for annotating entities with proper names. This is also known as entity extraction, chunking and identification. Common categories that are used for this type of text annotation include names of organizations, locations, persons, numerical values, month or time and day of the week, etc.
- Relation Extraction
This is the process for linking entities to better understand the structure of the text and the relationships between entities. In this example, the purpose is to understand customer orders: more specifically, to be able identify the 4 sub orders contained in this email.
- Keyphrase Tagging
This is a procedure for locating keyphrases or keywords in text. Also known as keyword extraction, this is often used to improve search-related functions for databases, ecommerce platforms, self-serve help sections of websites and so on.
- Part-of-Speech ( POS ) Tagging
This is where the functional elements of speech within the text data is annotated. These are often adjectives, nouns, adverbs, verbs and so on. The most common use case for this type of text annotation is for sentiment analysis and classification.
- Text Classification
Text classification is the process of annotating an entire body or line of text with a single label. This is where categories and tags are assigned to contextual data within lines or blocks of text. This is often used for labelling topics, detecting spam, analyzing intent and emotional sentiment. Here are a few use cases where a text annotation worksheet can be developed and used to train your ML and DL models:
- Document Classification
Still under text classification, tagging documents is used for efficiently sorting text-based content. This is often used by organizations like academic institutions and businesses for their public and private databases of contextual resource materials, and also for their collaborative publishing, editing and peer-review requirements.
- Product Categorization
Product categorization is the process of sorting particular products or services into classes and categories. This is often used by ecommerce platforms for improving search relevance, product recommendations and overall user experience.
- Sentiment Annotation
Sentiment-annotated text data is used to train datasets for ML tasks in NLP. Since it’s sometimes difficult even for humans to manually guess the true emotion behind a text message or email, this is a challenging field in NLP, ML and DL,. Remember, it’s non-trivial for a machine or even for human readers to spot, index and identify emotional connotations in text like humor, sarcasm or any other form of natural communication.
- Entity Linking
Entity annotation is the process of annotating certain entities within text data. This is often used for improving search-related functions and user experience.
Plus, entity linking is the process of connecting entities to larger repositories of data. This mainly involves linking labeled entities within text data to a URL ( uniform resource locator ), which offers more information about the entity.
How to speed up text annotation with the relevant tools?
Text annotation tools are programs that make it quicker and easier for data scientists and development firms to perform any of the text annotation methods described above. Some of these are packaged as libraries and modules with support for popular languages used in data science and machine learning, such as Python.
Meanwhile, there are also Web-based text annotation tools. This is where you’re provided with an administrator panel for managing, deploying and monitoring your text annotation requirements for your ML and DL models.
Functions for collaborative activities are integrated into a few of these text annotation tools. These are mostly libraries, modules and Web-based platforms with private remote databases and online functionality.
But keep in mind, NLP datasets are often huge collections of text data. This means it’s much faster and more cost-effective to delegate your text annotation requirements to a large group of remote workers and in-house QA ( quality assurance ) editors.
Still, it’s also usually challenging to assign and ensure the quality of text annotation projects, especially when you’re working with large numbers of workers. This is why the primary objective of these solutions is to make it simpler, extensible and more efficient to assign, review and perform quality assurance procedures on the text annotation output of crowdsourced teams of remote virtual workforces and in-house manpower resources.
Kili Playground is an example of a multi-purpose annotation tool. It provides a combination of these features. This quick and simple to use Python library is developed by Kili Technology. It offers a convenient way for data scientists, businesses, academic groups, AI researchers, ML and DL developers to cost-effectively delegate, monitor, edit and collaborate with virtual workforces and in-house associates for their image, speech and text annotation requirements.