Text data is the most common and widely used mode of communication. With the commencement of AI-driven solutions and the evolution of deep learning algorithms, text data has come under the broader field of NLP(Natural Language Processing). Named entity extraction has now been the core of NLP, where certain words are identified out of a sentence.
Another application is sentiment analysis where the meaning or tone of the sentence is extracted to understand it is positive, negative or neutral. Advanced models could also say if its happy, sad, sarcastic or rude. Such applications are used all over the internet in social media or eCommerce sites(product reviews).
Then there are chatbots or question-answer applications where system interacts with humans. Many more applications like document parsing, automatic summarization, lemmatization, tokenization have been developed around NLP. To build such complex models, the system needs to be trained with millions of labelled data. Manually labelling is tedious, costly(crowdsourcing) and time-consuming, so an alternative to such work is to make use of automatic ML-assisted text data annotator tools.
Earlier in the series of data annotator, we have discussed SuperAnnotate, LabelBox, and Playment. Today we will be talking about one such natural language annotation tool called Datasaur.
What is Datasaur?
Datasaur develops AI-based enterprise and production tools designed for data labelling in natural language processing. The company was launched by Ivan Lee in 2019 and is headquartered in Sunnyvale, California. It enables multiple user group interactions for efficient workforce management and uses its intelligent review tool to identify where they disagree provided by the report dashboard. Datasaur improves the quality of the training data by using pre-trained models to train the data. API support to directly import data from your production databases. And export to a wide variety of data formats(TSV, IOB,CSV, XLSX, JSON). Provides data security and privacy.
Features
Named Entity Recognition(NER) – Discovering specific words preferably nouns in a sentence which are called as entities and give meaning to the sentence itself. Entities are classified into real-world objects such as person, location, organization, etc.
Parts of Speech and Coreference Resolution – Identifying figures of speech that is the English grammatical parts in a sentence and finding out all expressions that refer to the same entity.
Dependency Resolution – subject dependencies with predicate
Document Labelling- categorizing text data in documents.
Image classification – answering questions or doing other operations based on images or videos.
OCR(Optical Character Reading) – converting text in images or documents to machine .readable text
Services:
- Financial – analyse terms and conditions mentioned in clauses, scan compliance, and categorize then
- Healthcare – extracting medical symptoms and diagnoses from audio recordings of physician encounters. Scans for medical journals papers. Classify and label medical claims.
- ECommerce – Sentiment analysis and invoice records by categorizing. Automate shipping and billing process by handling orders.
- Legal – extract terms from contracts. Automated legal research and litigation prediction
- Media – Customer sentiment analysis by monitoring activity.
Use Cases:
- Misinformation detection – extracts misleading facts from articles.
- Contract summarization & understanding – extract keynotes and main points from documents and flag unusual parts.
- Product review analysis – identify customer reviews and provide a thorough insight.
- Customer service call transcripts – combine NLP to the audio to text and understand customer issues.
- Receipt & invoice understanding – extract date, price and other details from invoices and save records.
Partnered Companies
CloudFactory, Daivergent, DataPure, Diffgram, iMerit, Ycombinator, Initialized(), StartX, G2.com. Datasaur recently partnered with NVIDIA NeMo toolkit for training conversational AI systems.
