With YZR's API and collaborative AI platform: - Business experts spend much less time correcting, tagging and grouping textual data manually with a NLP-powered solution - Data and IT teams integrate faster and with more confidence automated textual data quality pipelines into ETLs, data lakes,...
Legal entity ownership extraction is an NLP solution that helps identify and classify legal parent and subsidiary organization names in an unstructured text. The solution takes a text file as input. The text can be sourced from documents such as financial statements and legal documents. The...
Model Package - Fulfilled on Amazon SageMaker
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This model is specialized on health-related text analysis in colloquial language within the domain of Public Health and Voice of Patients. It is designed to identify and extract various entities such as Diagnosis, Treatments, Tests, Psychological Conditions, Relationship Status, Symptoms,...
Model Package - Fulfilled on Amazon SageMaker
At Lelapa AI, we're committed to advancing language technology and broadening its reach with our specialized Vulavula Multilingual Named Entity Recognition (NER) Model, particularly designed for Africa's linguistic diversity. This innovative model efficiently identifies and categorizes named...
Model Package - Fulfilled on Amazon SageMaker
Mphasis DeepInsights Named Entity Recognizer is an efficient way of identifying named entities present in the corpus of text. This solution applies NLP techniques to extract the named entities which can be used for further text analytics and for providing useful insights about the text. The model...
Model Package - Fulfilled on Amazon SageMaker
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The Clinical De-Identification model is designed to recognize and anonymize PHI in English-language clinical notes. It employs state-of-the-art natural language processing techniques to detect sensitive information such as patient names, addresses, medical record numbers, and other identifiers....
Model Package - Fulfilled on Amazon SageMaker
Free Trial
This model is specialized in health-related text analysis in colloquial language within the domain of Public Health and Voice of Patients. It is designed to identify and extract various entities such as Diagnosis, Treatments, Tests, Psychological Conditions, Relationship Status, Symptoms,...
Model Package - Fulfilled on Amazon SageMaker
Free Trial
This SageMaker model package provides a REST api to detect named entities in US English sentences. The API accepts input as JSON and identifies entities in an input text array. We welcome your feedback at aws-support@sigmodata.com. To see a notebook with usage instructions go to...
Model Package - Fulfilled on Amazon SageMaker
Free Trial
EXL XtraktoAI™ Medication Extraction solution focuses to extract and normalize drug data stored in free text fields of Electronic Health Record (EHR) or unstructured medical records or any document. The solution maps the extracted drugs with its respective RxCUI associated with RxNorm. It also...
Model Package - Fulfilled on Amazon SageMaker
This is a Named Entity Recognition model built upon a Transformer model from [Hugging Face](https://huggingface.co/elastic/distilbert-base-cased-finetuned-conll03-english). It takes a text string as input and predicts named entities in the input text. The deployed model can be used for running...
Model Package - Fulfilled on Amazon SageMaker
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