according to findings so far Micro understanding is the extracting of individual entities, facts, or relationships from the text. so findings approaches and algorithms to performing that provide micro understanding are important. highly appreciate all of yours valuable feedback on this.
Hi.
I'd like to recommend you to consider the Stanford Open Information Extraction: https://nlp.stanford.edu/software/openie.html. It implies the search for relations between entities. The principle of the work is described there:
Gabor Angeli, Melvin Johnson Premkumar, and Christopher D. Manning. Leveraging Linguistic Structure For Open Domain Information Extraction. In Proceedings of the Association of Computational Linguistics (ACL), 2015.
Moreover, you can test this functionality (coreference resolution, entities extraction, relations, etc.) on this page: https://corenlp.run/.
Have a look at L Article MEANS: A medical question-answering system combining NLP tec...
https://arxiv.org/ftp/arxiv/papers/1904/1904.01655.pdf See also:
https://lgatto.github.io/IntroMachineLearningWithR/example-datasets.html#the-diamonds-data
Please check the following articles. They may be useful.
https://www.researchgate.net/post/How-to-extract-particular-section-from-text-data-using-NLP-in-Python
https://www.analyticssteps.com/blogs/7-natural-language-processing-techniques-extracting-information
https://www.kdnuggets.com/2019/01/solve-90-nlp-problems-step-by-step-guide.html
https://rapidapi.com/blog/best-nlp-api/
https://www.accenture.com/us-en/blogs/search-and-content-analytics-blog/natural-language-processing-techniques
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