Search results
Results from the WOW.Com Content Network
Information extraction is the part of a greater puzzle which deals with the problem of devising automatic methods for text management, beyond its transmission, storage and display. The discipline of information retrieval (IR) [ 3 ] has developed automatic methods, typically of a statistical flavor, for indexing large document collections and ...
Spark NLP for Healthcare is a commercial extension of Spark NLP for clinical and biomedical text mining. [10] It provides healthcare-specific annotators, pipelines, models, and embeddings for clinical entity recognition, clinical entity linking, entity normalization, assertion status detection, de-identification, relation extraction, and spell checking and correction.
Retrieval Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.
A 3.1 TB dataset consisting of permissively licensed source code in 30 programming languages. Filtered through license detection and deduplication. 6 TB, 51.76B files (prior to deduplication); 3 TB, 5.28B files (after). 358 programming languages. Parquet Language modeling, autocompletion, program synthesis. 2022 [402] [403]
Python is a high-level, general-purpose programming language that is popular in artificial intelligence. [1] It has a simple, flexible and easily readable syntax. [2] Its popularity results in a vast ecosystem of libraries, including for deep learning, such as PyTorch, TensorFlow, Keras, Google JAX.
He joined Cogent Labs, a Japanese Deep Learning/AI company, in 2017. [4] He is a Machine Learning Engineering Manager at Mercari, Inc. [ 5 ] Cournapeau has also been involved in the development of other central numerical Python libraries: NumPy and SciPy .
Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.
Plagiarism in computer source code is also frequent, and requires different tools than those used for text comparisons in document. Significant research has been dedicated to academic source-code plagiarism. [47] A distinctive aspect of source-code plagiarism is that there are no essay mills, such as can be found in traditional plagiarism ...