Natural Language Processing Services and Solutions
Empowering teams to build world-leading AI products.
Human intelligence to transform Natural Language Processing (NLP) into a high-quality dataset for machine learning
Words alone fail to communicate the whole story. We at Shaip can help you train your AI models to interpret the ambiguity in human language
For quite some time, there has been deliberation on how Artificial Intelligence (AI) is set to change every aspect of human lives, and by now you must have already realized that it has the potential to be the most disruptive technology ever. Today we can talk to Siri, Cortana, or Google to get our basic queries addressed, but much of their actual potential is yet unknown
AI Systems can realize their full potential with natural language processing (NLP). Without NLP Services, AI can understand the meaning and answer simple questions, but it will fail to understand the context of what is being said. NLP solutions allows users to interact with intelligent systems in their very own language by reading text, understanding speech, interpreting what is said, and tries to measure human sentiment. It allows computers to learn and reply by replicating the human ability to understand everyday language that people use. The NLP algorithms can find patterns and can create inferences on their own. This can be achieved only if they receive accurately annotated training data in large volumes, which helps them to identify, understand, and indicate different elements in the language.
Data Collection Services
Data Annotation Services
Properly organized and precisely annotated data is at the heart of what makes Artificial (AI) / Machine Learning (ML) models work. Our proprietary platform and curated crowd management workflows, combines different tasks with the qualified worker, enabling consistent and low-cost delivery of high-quality output. Data can be annotated for a large number of use cases including Named Entity Recognition, Sentiment Analysis, Text & Audio Annotation, Audio Tagging, etc.
Data Licensing: Off-the-Shelf NLP Datasets
Browse through our audio dataset of diverse off-the-shelf NLP datasets, comprising of over 20,000 hours of audio, on a variety of topics such as Call-center, General Conversation, Debates, Speeches, Talks, Documentary, Events, General Conversation, Movie, News etc., in over 40 languages.
We offer a skilled resource that becomes an extension of your team to support your data annotation tasks, through tools that you prefer while maintaining the desired quality. Our experienced workforce understand the subtleties in human languages and apply the best practices learned by labeling millions of audio & text documents to deliver world-class data labeling solution for natural language processing.
Natural Language Processing Consulting and Implementation
Text and Audio Collection & Annotation Capabilities
From text/audio collection to annotation, we bring a greater understanding of the spoken world with detailed, accurately labeled text and audio to improve the performance of your NLP models. Whether you’re training a virtual/digital assistant, want to review legal contract, or build financial analysis algorithm, we provide the gold-standard data you need to make your models work in the real world. Our team understands the language, dialect, syntax, & sentence structure to accurately tag text, based on your business requirement.
We are one of the very few NLP companies that takes pride in their strong linguistic ability. We have global workforce of over 30,000 collaborators from across the globe, having expertise in over 150 languages. We’ve helped early-stage startups, small & medium enterprises, and worked with top fortune 500 companies across different verticals i.e., healthcare, retail/e-commerce, finance, technology, and more to achieve their NLP project goals.
Conversational AI Dataset / Audio Dataset
Over 50k hours of off-the-shelf audio/speech datasets to get you going.
NLP Datasets for Sentiment Analysis
Analyze human emotion by interpreting nuances in client reviews, social media, etc.
Text Dataset for voice recognition and chatbots
Collect text datasets i.e., emails, SMS, blogs, documents, research papers etc.
Conversational AI / Chatbot Training
Training digital assistants require a large set of quality data from different geographies, languages, dialects, set-ups, and formats. At Shaip, we offer training data for AI Models with Human-in-the-loop who have the required knowledge, domain expertise, and are well aware of the specific needs of the client.
Sentiment / Intent Analysis
It is rightly said, that words alone fail to communicate the whole story, and the onus lies on human annotators to interpret the ambiguity in human language. Hence identifying the Sentiment of a customer, based on the conversation is of utmost importance. Our language experts from various domains can interpret nuances in product reviews, financial news, and social media.
Named Entity Recognition (NER)
Named Entity Recognition (NER) is identifying, extracting, and classifying the named entities within a text, into pre-defined categories. The text could be categorized as a place, name, organization, product, quantity, value, percentage, etc. With NER you can address real-world questions such as which organizations were mentioned in the article etc.
Client Service Automation
Robust, well-trained Virtual Chatbots or Digital Assistants have revolutionized the way customers communicate with the sellers adding to significant improvement in customer experience.
From doctors’ handwritten prescriptions to conference calls notes, our specialists can digitize any form of data i.e., archived documents, legal contracts, patientt health records, etc.
Categorization also known as classification or tagging is the process of classifying text into organized groups and labeling it, based on its features of interest.
Topic Analysis or topic labeling is identifying and extracting meaning from a given text by identifying recurrent topics/themes under consideration.
Transcribe speech/podcast/seminar,call conversation into text. Leverage humans to accurately annotate audio/speech files to train NLP models accurately.
Categorize sounds or utterances to classify speech/audio based on language, dialect, semantics, lexicons, etc.
Dedicated and trained teams:
- 30,000+ collaborators for Data Creation, Labeling & QA
- Credentialed Project Management Team
- Experienced Product Development Team
- Talent Pool Sourcing & Onboarding Team
Highest process efficiency is assured with:
- Robust 6 Sigma Stage-Gate Process
- A dedicated team of 6 Sigma black belts – Key process owners & Quality compliance
- Continuous Improvement & Feedback Loop
The patented platform offers benefits:
- Web-based end-to-end platform
- Impeccable Quality
- Faster TAT
- Seamless Delivery
Computing setups, even with well-defined AI capabilities, find it hard to gauge the sentiment behind the queries. Natural Language Processing is one of the more seasoned branches of Artificial Intelligence that trains the machines better when it comes to understanding, analyzing, and responding to voice and textual data, thereby focusing on intelligent context determination behind responses.
Human languages are prone to variance and ambiguities. NLP setups, tools, and components aim at translating the text into several languages, responding accurately to verbal commands, analyzing sentiments, and recognizing entities, provided they are being trained with insanely high volumes of annotated data, covering every aspect of the human dialects.
If you seek actionable NLP examples that have been around for long, consider the predictive text analyzing tool on your smartphone as an acceptable starting point. Other examples include virtual assistants, including Bixby, Siri, Alexa, or more, spam box of your email platform, and the Google Translate
Upon much deliberation, it is clear that NLP-powered tasks mostly concern breaking down voice and text data to make the computer understand the context of the ingested data. Therefore, NLP is best used for text summarization, sentiment analysis over social media, training chatbots and VAs better, machine translation, and spam detection, used by readability and grammar checking tools and email platforms.
NLP can be further segregated into 5 components, with Lexical analysis for expressions and words, Semantic analysis for the meaning, Pragmatic analysis for interpretation, Syntax analysis for sentence structuring, and Discourse Integration for ascertaining sentence meaning as conveyed by connected sentences.