Easy-to-use Text Annotation Tool

With Kili Technology, annotating text is super-simple, regardless of the specific task to be completed. You can upload your text assets and then simply categorize them with one click; you can look for and label specific text entities and their relations inside the text file; or you can upload a PDF file and use bounding boxes and pre-OCR'd metadata to create a detailed map of your document's structure. Whatever the task, we got your back.

Sure, here are the most common use cases for text annotation:

1. 1. Customer Support and Chatbots: ML projects in customer support often utilize labeled text assets for training chatbots or virtual assistants. Labeled customer queries, along with corresponding responses or categorizations, can help train models to understand and respond accurately to customer inquiries, improving the overall customer experience.

2. 2. Email Filtering and Spam Detection: Labeled email datasets are commonly used to develop ML models for email filtering and spam detection. By annotating emails as spam or non-spam, you can teach a model to identify and prioritize incoming messages, reducing the burden on users by automatically filtering out unwanted or malicious content.

3. 3. News Categorization: ML projects in news categorization involve labeling news articles with predefined categories or topics. By annotating articles based on subject matter, such as politics, sports, technology, or entertainment, models can categorize and organize news content more efficiently, enabling personalized news recommendations and content aggregation.

4. 4. Sentiment Analysis in Social Media: Labeled text assets, such as tweets or social media posts, are used in sentiment analysis projects. By annotating these texts with sentiment labels (positive, negative, neutral), ML models can analyze public opinion, monitor brand sentiment, or track user sentiment trends for various products, services, or events.

Here are three popular and widely used tools in the field:

1. 1. NLTK is a comprehensive library that provides a wide range of functionalities for tasks such as tokenization, stemming, lemmatization, part-of-speech tagging, syntactic parsing, and more. NLTK also includes various corpora and pre-trained models, making it a valuable resource for NLP research and development.

2. 2 spaCy is a powerful and efficient NLP library that focuses on providing fast and streamlined processing capabilities. It offers robust features for tokenization, named entity recognition (NER), part-of-speech tagging, syntactic dependency parsing, and more. spaCy is known for its speed and ease of use, making it a popular choice for both research and production-grade NLP applications.

3. 3. Transformers (Hugging Face) is a state-of-the-art library developed by Hugging Face, which provides a wide range of pre-trained models for various NLP uses. It offers models that have achieved significant advancements in tasks such as text classification, question answering, named entity recognition, and text generation. The library also provides easy-to-use interfaces for fine-tuning and using these models, enabling developers to leverage the latest advancements in NLP with minimal effort.

While the manual annotation process ensures high accuracy and flexibility, it can be time-consuming and resource-intensive, especially for large datasets. Here's a few ideas on how to speed things up:

1. 1. Rule-based labeling involves creating predefined rules or patterns to assign labels to text based on specific patterns or criteria. For example, in sentiment analysis, a rule could be set to automatically label text as positive if it contains certain positive words or phrases. Rule-based labeling can be effective when the labeling task follows clear and well-defined patterns, but it may lack the flexibility to handle complex or ambiguous cases.

2. 2. ML-based labeling utilizes machine learning algorithms to automatically assign labels to text. This approach involves training a model on a labeled dataset using supervised learning techniques. The model learns from the patterns and features in the labeled data to predict labels for new, unlabeled text instances. ML-based labeling can significantly speed up the labeling process once the model is trained.

In practice, these labeling approaches can be used individually or in combination, depending on the specific requirements of the task, available resources, and desired trade-offs between accuracy, speed, and scalability.

There are several tools and frameworks available that can assist you in building a chatbot. Here are a few popular options:

1. 1. Dialogflow is a platform that offers a comprehensive set of tools for developing conversational agents. It provides natural language understanding capabilities, context management, and integration with various messaging platforms. Dialogflow supports both text-based and voice-based chatbots and offers a user-friendly interface for building and training chatbot models.

2. 2. IBM Watson Assistant is a powerful artificial intelligence platform that allows you to create and deploy chatbots across multiple channels. It offers advanced natural language understanding, entity recognition, and intent classification capabilities. Watson Assistant provides a visual dialog builder, allowing you to create conversational flows and integrate with backend systems easily.

3. 3. Microsoft Bot Framework is a comprehensive development framework for building chatbots. It supports multiple programming languages, including C#, JavaScript, and Python. The framework provides tools, SDKs, and connectors to create and deploy chatbots across various platforms, such as Microsoft Teams, Slack, and Facebook Messenger.

4. 4. Rasa is an open-source framework for building AI-powered chatbots. It provides a flexible and customizable platform for developing both rule-based and ML-based conversational agents. Rasa offers natural language understanding, dialog management, and integration capabilities, allowing you to build sophisticated chatbots with control over the entire conversational flow.

The choice of tool depends on factors such as your technical expertise, desired functionality, integration requirements, and deployment options. It's important to evaluate the features, documentation, community support, and scalability of the tools to determine the best fit for your specific chatbot project.

Multiple formats exist on the market, but the most common ones include:

1. 1. Brat/Standoff formats are widely used for text annotation. They involve representing the annotated text by using inline annotations or by associating annotations with specific character offsets. These formats typically include annotations such as entity mentions, relations, events, and attributes.

2. 2. IOB format is commonly used for named entity recognition (NER). It represents each token in a text sequence with a label that indicates whether it is inside an entity, at the beginning of an entity, or outside any entity.

3. 3. CoNLL format is often used for dependency parsing and syntactic annotation. It represents each token in a sentence with various fields, including the word, part-of-speech tag, syntactic head, and dependency relation. CoNLL format enables the representation of the syntactic structure of sentences.

On top of these specialized formats, one can also use standard XML or JSON files to transfer annotated data. The choice of a specific format depends on the annotation task, tools used, and the desired compatibility with other systems or datasets.

If you're into scripting your tasks, Kili Technology is the right tool for you. You can use our API to add prediction or inference-type annotations generated by your pre-trained model and process your data quickly and efficiently. You can also use webhooks or Kili plugins to create and run your own code in the background (for example, to correct well-known issues automatically), thus further streamlining the whole label creation and review process.

Sure! Multiple users with varying roles and responsibilities can collaborate on a Kili Technology project. What's more, you can decide to apply QA measures aimed specifically at collaborative labeling, like consensus. Consensus works by having more than one labeler annotate the same asset. When the asset is labeled, a Consensus score that measures the inter-annotator agreement level is calculated for a given asset and shown in labeling stats in the app. This is one of key measures for controlling label production quality.

Of course, Kili Technology can help with that. Regardless of whether you're looking to urgently scale up your existing project or simply want to save some valuable time you'd otherwise spend on a long recruitment process, we're here for you. Through Kili, you can hire qualified labelers with years of experience in many different industries, get your datasets properly labeled within days and have your production-quality models ready and deployed shortly after.

What's more, we only partner with ethical workforce providers that are certified in enforcing gender, age and race equality in the workplace (B Corporation Certified). This way, we help ensure that your datasets are bias-free and diverse.

Feel free to browse our article on data labeling service providers for further information.

There are many possible project ontologies that you can build with Kili Technology. The specific one you'll be using for text annotation will depend on the task you're trying to accomplish. Your ontology designed for a simple classification task will probably be quite similar to the one built for classifying images. Though if your project involves adding specific labeling jobs designed for working with textual data, like named entity recognition, marking relations between text entities, text transcription, or OCR, your ontology will be very different.