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https://code.google.com/archive/p/word2vec/
Software tool which provides implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words. These representations can be used in many natural language processing applications and for further research. It takes a text corpus as input and produces the word vectors as output. It first constructs a vocabulary from the training text data and then learns vector representation of words. The resulting word vector file can be used as features in natural language processing and machine learning applications.
Proper citation: word2vec (RRID:SCR_014776) Copy
http://stanfordnlp.github.io/CoreNLP/
A Java suite of core natural language analysis tools. It can take raw human language text input and give multiple outputs, including the base forms of words, their parts of speech, and marked up structure of sentences in terms of phrases or word dependencies. Supported languages include English, Arabic, Chinese, French, German, and Spanish.
Proper citation: Stanford CoreNLP (RRID:SCR_014778) Copy
https://github.com/ferhtaydn/biocemid/tree/odj
A manually annotated data set of full text articles and a tool for identifying passages describing experimental methods for physical protein-protein interactions.
Proper citation: Biocemid (RRID:SCR_014779) Copy
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Welcome to the Neuroscience Information Framework Project, designed to serve the biomedical research community. NIF maintains the largest searchable collection of neuroscience data, the largest catalog of biomedical resources, and the largest ontology for neuroscience on the web. We welcome all feedback and suggestions and are actively looking for resource providers to make their resources accessible through NIF. Learn about the tools available to help you share your data and discover a dynamic inventory of Web-based neuroscience resources.
