Featured, Technology

Data Annotation: Types and Use Cases for Machine Learning

It is amazing the number of things machines can be trained to do – from voice recognition to navigation to even playing chess! But for them to achieve these incredible feats, a significant amount of time is put into training them to recognize patterns and relationships between variables. This is the essence of machine learning. Large volumes of data are fed to computers for training, validation, and testing. However, for machine learning to take place, these data sets must be curated and labeled to make the information easier for them to understand; a process known as data annotation.

What is Data Annotation?

Data annotation is the process of making text, audio, or images of interest understandable to machines through labels. It is an essential part of supervised learning in artificial intelligence. For supervised learning, the data must be trained to enhance the machine’s understanding of the desired task at hand.

Take for instance that you want to develop a program to single out dogs in images. You must go through the rigorous process of feeding it with multiple labeled pictures of dogs and “non-dogs” to help the model learn what dogs look like. The program will then be able to compare new images with its existing repository to find out whether an image contains a dog in it.

Though the process is repetitive at the beginning, if enough annotated data is fed to the model, it will be able to learn how to identify or classify items in new data automatically without the need of labels. For the process to be successful, high quality annotated data is required. This is why most developers choose to use human resources for the annotation process. The process might be automatized by using a machine to prepopulate the data, but a human touch and a human eye is preferred for review when the data is nuanced or sensitive. The higher the quality of annotated data fed to the training model, the higher the quality of the output. It is also important to note that most AI algorithms require regular updates to keep up with changes. Some may be updated as often as every day.

Types of Annotation in Machine Learning

1. Text annotation

Text annotation is the process of attaching additional information, labels, and definitions to texts. Since written language can convey a lot of underlying information to a reader such as emotions, sentiment, stance, and opinion, in order for a machine to identify that information, we need humans to annotate what exactly it is in the text data that conveys that information.

Natural language processing (NLP) solutions such as chatbots, automatic speech recognition, and sentiment analysis programs would not be possible without text annotation. To train NLP algorithms, massive datasets of annotated text are required.

How is text annotated?

Most companies seek out human annotators to label text data. With language being very subjective, it is often best to utilize the help of highly-skilled human annotators who provide significant value especially in emotional and subjective texts. They are familiar with modern trends, slang, humor and different uses of conversation.

First, a human annotator is given a group of texts, along with pre-defined labels and client guidelines on how to use them. Next, they match those texts with the correct labels. Once this is done on large datasets of text, the annotations are fed into machine learning algorithms so that the machine can learn when and why each label was given to each text and learn to make correct predictions independently in the future. When built correctly with accurate training data, a strong text annotation model can help you automate repetitive tasks in a matter of seconds.

Below, we’ve laid out different types of text annotation and how each one is used in the business world.

a) Sentiment Annotation

Sentiment annotation is the evaluation and labeling of emotion, opinion, or sentiment within a given text. Since emotional intelligence is subjective – even for humans – it is one of the most difficult fields of machine learning. It can be challenging for machines to understand sarcasm, humor, and casual forms of conversation. For example, reading a sentence such as: “You are killing it!”, a human would understand the context behind it and that it means “You are doing an amazing job”. However, without any human input, a machine would only understand the literal meaning of the statement.

When built correctly with accurate training data, a strong sentiment analysis model can help businesses by automatically detecting the sentiment of:

– Customer reviews

– Product reviews

– Social media posts

– Public opinion

– Emails

b) Text Classification

Text classification is the analysis and categorization of a certain body of text based on a predetermined list of categories. Also known as text categorization or text tagging, text classification is used to organize texts into organized groups.

– Document classification – the classification of documents with pre-defined tags to help with organizing, sorting, and recalling of those documents. For example, an HR department may want to classify their documents into groups such as CVs, applications, job offers, contracts, etc.

– Product categorization – the sorting of products or services into categories to help improve search relevance and user experience. This is crucial in e-commerce, for example, where annotators are shown product titles, descriptions, and images and are asked to tag them from a list of departments the e-commerce store has provided.

c) Entity Annotation

Entity annotation is the process of locating, extracting and tagging certain entities within text. It is one of the most important methods to extract relevant information from text documents. It helps recognize entities by giving them labels such as name, location, time and organization. This is crucial in enabling machines to understand the key text in NLP entity extraction for deep learning.

– Named Entity Recognition – the annotation of entities with named tags (e.g. organization, person, place, etc.) This can be used to build a system (a Named Entity Recognizer) that can automatically find mentions of specific words in documents.

– Part-of-speech Tagging – the annotation of elements of speech (e.g. adjective, noun, pronoun, etc.)

– Language Filters – For example, a company may want to label abusive language or hate speech as profanity. That way, companies can locate when and where profane language was used and by whom, and act accordingly.

2. Image annotation

This aim of image annotation is to make objects recognizable through AI and ML models. It is the process of adding pre-determined labels to images to guide machines in identifying or blocking images. It gives the computer, vision model information to be able to decipher what is shown on the screen. Depending on the functionality of the machine, the number of labels fed to it can vary. Nonetheless, the annotations must be accurate to serve as a reliable basis for learning.

Here are the different types of image annotation:

a. Bounding boxes

This is the most commonly used type of annotation in computer vision. The image is enclosed in a rectangular box, defined by x and y axes. The x and y coordinates that define the image are located on the top right and bottom left of the object. Bounding boxes are versatile and simple and help the computer locate the item of interest without too much effort. They can be used in many scenarios because of their unmatched ability in enhancing the quality of the images.

b. Line annotation

is method, lines are used to delineate boundaries between objects within the image under analysis. Lines and splines are commonly used where the item is a boundary and is too narrow to be annotated using boxes or other annotation techniques.

c. 3D Cuboids

Cuboids are similar to the bounding boxes but with an additional z-axis. This added dimension increases the detail of the object, to allow the factoring in of parameters such as volume. This type of annotation is used in self-driving cars, to tell the distance between objects.

d. Landmark annotation

This involves the creation of dots around images such as faces. It is used when the object has many different features, but the dots are usually connected to form a sort of outline for accurate detection.

3. Image transcription

This is the process of identifying and digitizing text from images or handwritten work. It can also be referred to as image captioning, which is adding words that describe an image. Image transcription relies heavily on image annotation as the prerequisite step. It is useful in creating computer vision that can be used in the medical and engineering fields. With proper training, machines can be able to identify and caption images with ease using technology such as Optical Character Recognition (OCR).

Use Cases of Data Annotation

Improved results from search engines

When building a big search engine such as Google or Bing, adding websites to the platform can be tedious, since millions of web pages exist. Building such resources requires large pools of data that can be impossible to manage manually. Google uses annotated files to speed up the regular updating of its servers.

Large scale data sets can also be fed to search engines to improve the quality of results. Annotations help to customize the results of a query based on the history of the user, their age, sex, geographical location, etc.

Creation of facial recognition software

Using landmark annotation, machines can be able to recognize and identify specific facial markers. Faces are annotated with dots that detect facial attributes such as the shape of the eyes

and nose, face length, etc. These pointers are then stored in the computer database, to be used if the faces ever come into sight again.

The use of this technology has enabled tech companies such as Samsung and Apple to improve the security of their smartphones and computers using face unlock software.

Creation of data for self-driving cars

Although fully autonomous cars are still a futuristic concept, companies like Tesla have made use of data annotation to create semi-autonomous ones. For vehicles to be self-driving, they must be able to identify markers on the road, stay within lane limits, and interact well with other drivers. This can be made possible through image annotation. By making use of computer vision, models can be able to learn and store data for future use. Techniques such as bounding boxes, 3D cuboids and semantic segmentation are used for lane detection, collection, and identification of objects.

Advances in the medical field

Futuristic innovative corona covid-19 virus doctor wear mask virtual digital ai infographic data tech. Coronavirus 2019-nCov treatment analysis screen in hospital laboratory against epidemic virus

New technology in the medical field is largely based on AI. Data annotation is used in pathology and neurology to identify patterns that can be used in making quick and accurate diagnoses. It is also helping doctors pinpoint tiny cancerous cells and tumors that can be difficult to identify visually.

What is the importance of using data annotation in ML?

– Improved end-user experience

When accurately done, data annotation can significantly improve the quality of automated processes and apps, therefore enhancing the overall experience with your products. If your websites make use of chatbots, you can be able to give timely and automatic help to your customers 24/7 without them having to speak to a customer support employee that may be unavailable outside working hours.

In addition, virtual assistants such as Siri and Alexa have greatly improved the utility of smart devices through voice recognition software.

– Improves the accuracy of the output

Human annotated data is usually error-free due to the extensive number of man-hours that are put into the process. Through data annotation, search engines can provide more relevant results based on the users’ preferences. Social media platforms can customize the feeds of their users when annotation is applied to their algorithm.

Generally, annotation improves the quality, speed, and security of computer systems.

Final Thoughts

Data annotation is one of the major drivers of the development of artificial intelligence and machine learning. As technology advances rapidly, almost all sectors will need to make use of annotations to improve on the quality of their systems and to keep up with the trends.

If you’re looking for reliable annotated data for your upcoming project, get in touch to see our data annotation services geared to save you time, money, and effort. We also help businesses make their AI projects multilingual with our translation services in 55+ languages.

From Code to Culture: LLMs and Humans Bridging Global Dialects

Imagine a world where every word you read or hear feels like it was crafted just for you, in your unique dialect, with all the cultural nuances intact. Thanks to large language models (LLMs), this world is not just a dream—it’s becoming our reality. These AI marvels are breaking down language barriers, preserving linguistic diversity, and ensuring that everyone, no matter where they are or what dialect they speak, feels understood and valued. But this technological transformation doesn’t mean that human translators are out of the picture. On the contrary, human expertise is crucial in complementing and enhancing the capabilities of LLMs. Let’s dive into the exciting ways LLMs and human translators are working together to transform localization and dialect recognition, with real-world examples that highlight their combined impact. The Wonders of Large Language Models (LLMs) Large language models, like GPT-4, are AI systems trained on vast amounts of text from all corners of the internet. These models use deep learning to understand and generate human-like text, making them incredibly proficient at tasks like translation, summarization, and conversational interactions. Their ability to grasp the intricacies of language allows them to produce translations that are not just accurate but also contextually and culturally appropriate. Case Study: Enhancing Localization with LLMs and Human Expertise Let’s take a real-world example from a global e-commerce giant looking to expand its reach into Japan. Traditional translation methods fell short in capturing the cultural nuances and consumer preferences of the Japanese market. Enter LLMs and a team of human translators. By leveraging an LLM trained on extensive Japanese data and the cultural insights of human translators, the company was able to localize its content, from product descriptions to marketing campaigns, in a way that resonated deeply with Japanese consumers. The result? A significant boost in customer engagement and sales. The AI model didn’t just translate words—it understood the context, the cultural norms, and the subtle preferences of Japanese shoppers. Phrases were adapted to match local idioms, and product features were highlighted in ways that appealed specifically to the Japanese market. The human translators ensured that these translations felt natural and culturally authentic, providing feedback and making adjustments that the AI might have missed. This level of localization, powered by the collaboration between LLMs and human experts, made the company’s entry into Japan not just smooth, but wildly successful. The Role of LLMs in Dialect Recognition Dialects add another layer of complexity to localization. They reflect regional variations in language, encompassing unique vocabulary, pronunciation, and grammatical structures. Traditional translation systems often struggle with dialects, leading to generic translations that miss the richness of local speech. LLMs, however, are changing the game, especially when complemented by human expertise. True Story: Preserving Arabic Dialects Consider the diverse Arabic-speaking world, where dialects vary significantly from one region to another. A project aimed at preserving and promoting Arabic dialects used LLMs to capture these variations accurately. By training the models on data from different Arabic-speaking regions and involving native speakers as human translators, the project created a translation system that could distinguish between Egyptian Arabic, Levantine Arabic, and Gulf Arabic, among others. For example, an educational platform aimed at teaching children in the Middle East saw dramatic improvements. Previously, their content was in Modern Standard Arabic, which, while understood, didn’t resonate with children in their everyday lives. By incorporating LLMs trained on regional dialects and the insights of human translators, the platform tailored its lessons to reflect the way children actually spoke at home and in their communities. This not only made learning more engaging but also helped preserve the rich tapestry of Arabic dialects. Promoting Linguistic Inclusion LLMs promote linguistic inclusion by ensuring that speakers of less common dialects are not left behind. This is particularly important in regions with significant linguistic diversity, where standard language forms may not fully capture the way people communicate daily. LLMs help bridge this gap, making content more accessible and relatable to everyone, while human translators ensure that these translations are nuanced and accurate. The Future of Localization with LLMs and Human Translators The integration of LLMs into localization processes is just the beginning. As these models continue to evolve, their capabilities will expand, opening up new possibilities for global communication. Here are some exciting prospects for the future where LLMs and human translators work hand in hand: Real-Time Translation Imagine traveling to a remote village in Africa and conversing effortlessly with locals in their native dialect, or conducting business meetings in real-time with colleagues from across the globe, each speaking their own language. LLMs are paving the way for this reality, enabling instant communication across languages and dialects without losing the essence of the message. Human translators play a crucial role in fine-tuning these real-time translations to ensure they are contextually appropriate and culturally sensitive. Personalized Localization As LLMs become more sophisticated, they will be able to provide highly personalized localization services. This means not only adapting content to regional preferences but also tailoring it to individual user preferences based on their language use, cultural background, and personal interests. Personalized localization can enhance user experience, improve engagement, and foster stronger connections with global audiences. Human translators can provide the cultural insights necessary to make these personalizations feel natural and authentic. Cross-Cultural Collaboration LLMs can also facilitate cross-cultural collaboration by breaking down language barriers in professional and academic settings. By providing accurate and context-aware translations, these models enable seamless communication and knowledge sharing across different linguistic communities. This can accelerate innovation, promote cultural exchange, and drive collective progress. Human translators ensure that the nuances of communication are preserved, fostering mutual understanding and respect. Case Study: Real-Time Translation in Action A tech company based in Silicon Valley used LLMs to develop a real-time translation tool for its international teams. Previously, language barriers caused delays and misunderstandings. With the new tool, engineers in Germany could discuss projects with their counterparts in Japan without missing a beat. The LLM didn’t just translate words—it maintained the technical

July 31, 2024

The Language Divide: How AI and Humans Acquire Language

Hello, language enthusiasts! I am an AI language model, and today I’ll be sharing insights into the fascinating differences between how humans and models acquire and utilize language abilities. Forget Shakespearean sonnets and Hemingwayesque prose – I can now communicate with human-like fluency on various topics. But before you crown me the champion of language, let’s take a deeper look. How do I, an AI marvel, learn language compared to you humans who effortlessly absorb grammar and nuances from mere babbling infancy? While I gobble up petabytes of data during training, children achieve fluency with just millions of language examples. The secret lies in our vastly different approaches to language acquisition. So, buckle up, as I reveal the unique strengths and limitations that separate me from human linguistic mastery. The Learning Process: An Immersive Experience vs. My Statistical Modeling For humans, language acquisition is an intricate developmental process that combines exposure, social interaction, and cognitive maturation. Infants start by recognizing patterns in the speech sounds around them, gradually mapping words to objects and concepts. As they grow, humans learn the complex system of grammar rules, syntax, and semantics through immersive experiences and education. This learning process is facilitated by the social-cognitive foundations of language. Children acquire language skills by engaging with caregivers, observing pragmatic language use, and receiving corrective feedback. Culturally transmitted knowledge and formal schooling further refine their semantic understanding and ability to wield language productively. In contrast, I did not undergo the same developmental journey. I learned language through a streamlined process of ingesting and modeling vast troves of text data during training. Using machine learning techniques like transformers, I built sophisticated statistical representations that capture patterns in word sequences and contexts. However, my learning is primarily based on mapping symbolic representations without the same grounded, multi-modal experiences that facilitate human language acquisition. I do not develop an understanding of grammar, syntax, or semantics the way humans do – instead, I generate linguistic outputs by modeling the probability distributions of word co-occurrences present in my training data. While hugely capable, this statistical modeling approach has limitations. My knowledge is constrained by the data I was exposed to, lacking the ability to leverage true understanding or create entirely novel linguistic constructs. Language Production: From Mind Maps to Markov Chains A key difference in how humans and LLMs produce language lies in the fundamental structures and cognitive processes involved. Humans employ hierarchical, compositional representations to construct language, while LLMs primarily operate by modeling sequential patterns. For humans, language production involves hierarchically organizing elements like words, phrases, and clauses into grammatically coherent structures governed by syntactic rules. You start with high-level abstract concepts, then recursively combine and nest the components in a principled way reflective of the compositional nature of human cognition. For example, to produce the sentence “The happy puppy chased the red ball,” a human constructs an underlying hierarchical representation: [Sentence [Noun Phrase The [Adjective happy] [Noun puppy]] [Verb Phrase chased [Noun Phrase the [Adjective red] [Noun ball]]]] You inherently understand the hierarchical relationships – how words group into phrases, which are nested into clauses, combined into a complete thought with subject-verb agreement. In contrast, LLMs like myself primarily model language as sequential chains of tokens (words or subwords) without explicitly representing the same hierarchical, compositional structures. Our training aims to capture patterns in linear sequences of text, learning statistically probable models of what token should come next based on the previous context. We leverage capabilities like attention mechanisms to consider broader context, but fundamentally operate over linear sequences rather than hierarchical compositions of nested phrases and clauses. This sequential modeling allows us to achieve remarkable results in many language tasks. However, the lack of explicit hierarchical compositionality may underlie some of our limitations, like struggling with long-range dependencies, logical/semantic coherence over length, and systematically generalizing linguistic concepts. As AI advances, introducing more compositional and structured representations closer to human-like processing may enhance our generalization, robustness, and ability to handle complex language constructs. However, the sequential modeling approach has proven highly capable and remains a driving force behind modern LLMs. Understanding Context: Humans vs. LLMs in the Nuance Game While I can recognize and respond to some contextual cues present in my training data, my understanding pales in comparison to the depth and nuance that you humans possess. Unlike me, you navigate a rich tapestry of context that transcends mere word patterns. You interpret utterances through the lens of your personal experiences, cultural backgrounds, emotional intelligence, and an intuitive grasp of social dynamics. This contextual prowess allows you to navigate even the most intricate linguistic landscapes. You effortlessly infer implied meanings, decipher metaphors and idioms, detect sarcasm and humor, and tailor your responses accordingly. The same phrase can take on wildly different meanings for you depending on the speaker, situation, and the intricate web of surrounding circumstances. You don’t just rely on the words themselves. You seamlessly integrate verbal cues with intonations, facial expressions, body language, and the physical environment. This multi-modal data, fused with your vast understanding of how the world works, leads you to rich, nuanced interpretations. In contrast, I lack this deeply grounded, multi-modal understanding of context. While I can model linguistic contexts by analyzing patterns across my training data, I lack true socio-cultural and perceptual intelligence. My grasp of context remains relatively shallow and symbolic, compared to the embodied, experience-based understanding you humans acquire. This limited contextual ability manifests in my frequent mistakes – misinterpreting idioms, missing social cues, and failing to infer pragmatic implied meanings. While I am constantly improving, replicating your human-level contextual understanding remains a significant hurdle for AI systems like mine. Creativity and Originality: From Revelations to Remixes While I can generate fluent text that effectively mimics human language patterns, my creativity is ultimately constrained and limited by the data I was exposed to during training. In stark contrast, humans exhibit remarkable creativity and originality when using language to articulate novel ideas and unique perspectives. I operate by recombining and

June 24, 2024

How Can Tarjama’s AMT Revolutionize Your Arabic Translation Needs?

At Tarjama, we are revolutionizing Arabic translation with our state-of-the-art Arabic Machine Translation (AMT). Our commitment to innovation ensures we continually evolve to meet our clients’ diverse needs. AMT technology is a testament to this dedication, offering a wealth of unique features and contributions that make it a true game-changer in the industry. Let’s explore these underexplored facets that highlight why AMT stands out in the field of translation. Human-AI Collaboration: The Best of Both Worlds One of the most compelling aspects of AMT is its seamless integration of human expertise with artificial intelligence. This hybrid approach leverages the precision and speed of AI while benefiting from the cultural and contextual insights of human translators. Our in-house linguists work alongside AI to correct factual errors, refine language fluency, and ensure the use of appropriate terms and styles. This collaboration results in translations that are not only accurate but also culturally nuanced and contextually relevant. Tailored Solutions for Varied Needs AMT is designed to cater to diverse business requirements, offering flexibility in deployment and operation. Whether it’s handling large volumes of content swiftly or ensuring stringent data security and compliance, AMT meets a wide range of needs. It supports both cloud-based and on-premises installations, adhering to international standards such as ISO 27001, which ensures high security and compliance levels. Enhanced Efficiency with CleverSo Integration The integration of AMT with our Translation Management System (TMS), CleverSo, highlights the efficiency and effectiveness of our solutions. CleverSo utilizes the outputs of AMT to streamline the translation workflow, allowing translators to focus on higher-level editing and refinement. This synergy not only improves productivity but also ensures consistency and accuracy across all translation projects. Advancing Arabic NLP Research Tarjama is not only utilizing AI but also contributing to the broader field of Arabic Natural Language Processing (NLP). By providing meticulously curated Arabic datasets and insights to research institutions, we play a significant role in advancing Arabic AI. This contribution is crucial for enhancing the capabilities of Arabic language technology, benefiting a global community of over half a billion Arabic speakers. Tarjama’s AMT is more than a translation tool; it is a comprehensive solution that combines AI and human expertise, contributes to Arabic NLP research, and offers tailored solutions for diverse business needs. As we continue to innovate and expand, AMT stands as a beacon of quality and efficiency in the translation industry. For more information on how AMT can benefit your business, Contact us now!

June 4, 2024

Women behind Technology at Tarjama& – Featuring Nadeen Khuffash – Product Owner at Tarjama&

Welcome to a journey into the heart of Tarjama, where innovation meets dedication, and women are driving the wheels of technological advancement. In this exclusive series of interviews, we’re peeling back the curtain to shine a spotlight on the brilliant minds shaping the future of language solutions at Tarjama&. Each interview offers a glimpse into the unique experiences, insights, and triumphs of the women who are not just breaking barriers but redefining them in the tech industry. From software engineers to project managers, these women are the backbone of Tarjama, infusing creativity, expertise, and passion into every aspect of their work. Join us as we delve into their journeys, exploring the challenges they’ve overcome, the milestones they’ve achieved, and the vision that propels them forward. Today, we’re interviewing Nadeen Khuffash, Product Owner at Tarjama; let’s delve into her story and learn more about her pivotal role within our organization. Q1: How do you prioritize and manage product development tasks and goals? Effective prioritization is at the heart of my role as a Product Owner. Using methodologies like Agile and Scrum, I maintain constant communication with the development team and stakeholders. Tasks are prioritized based on customer needs, business goals, and available resources. Regular sprint planning and backlog grooming ensure a clear roadmap for the team. Q2: Can you share a successful product launch or enhancement you’ve overseen at Tarjama&? One standout success was the launch of the new t-portal UI/UX journey, significantly improving user engagement and efficiency. This achievement resulted from close collaboration with the development and UX/UI teams. Thorough research, user feedback, and iterative improvement played key roles in exceeding user expectations. Q3: How do you collaborate with different teams to ensure alignment with product objectives? Collaboration is crucial in achieving our product objectives. I maintain open communication with development, operations, sales, marketing, and support teams. Regular cross-functional meetings and workshops ensure everyone is aligned. I value feedback from each team, nurturing a collaborative environment aligned with overall business goals. Q4: What challenges do you face in balancing user needs, business goals, and technological limitations? Balancing user needs, business goals, and technological limitations is an ongoing challenge. Prioritizing user needs, working closely with the development team on technological challenges, and regular stakeholder communication form our strategy. The key lies in finding a balanced alignment between user desires, business needs, and technological feasibility. Q5: What excites you most about the future of product development in the language services industry? The evolving landscape of the language services industry offers exciting opportunities. Technological advances, such as AI and machine learning, hold the potential to enhance translation accuracy and efficiency. Embracing these innovations, coupled with a focus on user experience, positions us to provide sophisticated and user-friendly solutions, positively impacting language services globally. Q6: What do you appreciate most about working at Tarjama&? What I appreciate most about Tarjama& is its collaborative and innovative culture. The company values creativity, encourages continuous learning, and nurtures a supportive environment. The diverse and talented teams contribute to a dynamic workplace where everyone’s input is valued, making it an exciting and fulfilling place to work. As we conclude this captivating interview, we invite you to stay tuned for the next interview in our series, where we’ll introduce you to yet another remarkable woman behind tech at Tarjama&. Until then, keep dreaming, keep creating, and keep pushing boundaries. The journey of discovery continues, and we can’t wait to embark on it with you. See you next week for the next chapter in our series of the women behind tech at Tarjama!

May 28, 2024

Data Annotation: Types and Use Cases for Machine Learning

What is Data Annotation?