Build Sophisticated Language Models With Precision Annotation
Natural language processing (NLP) has transformed the way we interact with machines.
Rapid development in the last decade has ushered forth a new era where machine learning (ML) models can understand complex human text and provide rich, accurate answers.
Supervised learning still forms the backbone of many language models, particularly for niche and domain-specific applications. Plus, language annotation is vital to ensure models are up-to-date, bias-free, and functional across multiple languages, dialects and cultural contexts.
Aya Data are language specialists.
Our NLP teams offer C1 / C2 level English, French and fluency in over 10 African dialects.
We understand that language is only half the battle. Aya Data believes that a comprehensive understanding of linguistics is required to reliably interpret syntax, colloquialism, accents, context and sentiment, so that your model can do the same.
Our NLP Annotation Services
Named Entity Recognition: Identify. Extrapolate. Organize.
Named entity recognition (NER) is a semantic information extraction technique that locates and categorizes relevant entity data from text at scale.
Named entities include everything from names and brands to addresses, locations, and virtually any other form of classifiable textual information. Named entity extraction makes these entities usable to NLP models.
NER for machine learning is essential in a wide range of commercial and non-commercial industries, such as social media analytics, customer services, translation, biomedicine, the natural sciences, cybersecurity, and the news and media.
Once trained, named entity detection algorithms derive meaning from text with minimal manual processing, vital for sentiment analysis, text analytics, and other NLP purposes.
Named entity extraction in NLP equips models with knowledge of how various entities interlink and cross-match between passages. This is vital for bringing coherency to NLP models that frequently parse entity-rich texts.
Aya Data has proven expertise in applying named entity recognition annotation in both commercial and non-commercial contexts.
Our team will label each named entity accurately and apply relationship extraction to link entities throughout the text.
Sentiment Analysis: Optimize Your Customer Relations with AI-Generated Analysis
Human text contains a huge range of sentiments that convey emotion, tonality, values, principles, and other affective states. Modern AIs are capable of understanding affective states at scale.
Sentiment and NLP text analysis is helping organizations understand the sentiments behind their brands and products, also enabling them to manage their reputations.
Sentiment Analysis For Businesses and Organizations
NLP sentiment analysis techniques enable businesses and organizations to make sense of complex textual data. NLP models are trained on sentiment analysis datasets to instruct them on understanding text's emotional or affective qualities.
For example, social media sentiments encapsulate everything from cultural ideas, norms, and knowledge to brand sentiments. Social media data analysis enables brands to home in on emerging trends and customer feedback.
Organizations can use sentiment analysis to analyze everything from customer service chat logs to feedback forms to uncover insights into brands, products, and concepts at scale.
Sentiment Analysis Using NLP
Sentiment analysis AI is trained using NLP techniques, including tagging and labeling sentiments to instruct the AI whether tones or emotions are broadly positive, negative, or neutral. However, it’s also possible to uncover finer layers of meaning.
Aya Data’s sentiment analysis services enable your business or organization to extract complex meaning from human text.
Our data experts are C1/C2 level linguists and can work in English, French, Arabic and over 10 African dialects.
Audio text transcription: Streamline your speech to text models with audio text transcription
Modern audio and text transcription can convert colossal volumes of audio files into text and vice-versa. Audio and voice transcription has become more accurate with regard to multi-speaker support and dealing with background or ambient noise.
Audio-to-text transcription enables organizations and businesses to disseminate data in multiple formats across their channels automatically.
For example, businesses transcribe audio of meetings, seminars, and events to multiply resources, create new marketing materials, and transcribe text to turn written content into narrations, commentary, and talk-overs. Text and writing transcription can improve business communications and expedite data conversion between audio and text formats.
Multiple speakers can be separated from recordings automatically, and algorithms can transcribe audio files with minimal manual processing, making transcribing audio to text much simpler in time-sensitive scenarios.
Transcribe Audio to Text
Aya Data’s audio transcription services will help you derive additional value from your audio and video resources.
We can transcribe audio files to text and provide voice recording transcription services with multi-speaker support.
What’s a Rich Text element?
The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.
Static and dynamic content editing
A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!
How to customize formatting for each rich text
Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.
- text for bullet points