Comments (3)
Hi! Indeed, we send the query directly to the semanticscholar api without any processing, so you can just follow their guide.
Expanding the information in the docstring sounds like a good idea. If you make a pull request to add that reference to the docstring, we would be more than happy to merge your contribution!
Let us know if you run into any other problems (either in code or in the documentation).
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This has been resolved by #86
Thanks for your contribution!
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Ok, I believe I have figured it out: https://www.semanticscholar.org/product/api%2Ftutorial#using-search-query-operators
So one example could be: docs = litstudy.search_semanticscholar("((cloud computing) | virtualization) +security", limit=10, batch_size=5)
Maybe this reference could be added to the docstring?
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Related Issues (20)
- 'No Edges Given' for Network Analysis HOT 4
- ValueError: n_components must be < n_features; got 50 >= 47 HOT 2
- `build_corpus` always removes words having a frequency below 5 HOT 4
- Different results from unique() and difference of deduplicated set HOT 2
- module 'networkx' has no attribute 'to_scipy_sparse_matrix' HOT 2
- Incompability with gensim 4 HOT 1
- Unexpected results from litstudy.plot_author_histogram() HOT 2
- Listing document titles HOT 3
- Support for google scholar HOT 1
- refine_scopus - low it/s speed; necessary to refine every time? HOT 1
- TypeError: object of type 'method' has no len() HOT 1
- Saving language models
- Search_semanticscholar with list
- Scopus400Error: Error translating query - Refining results with "source title" query argument HOT 6
- train_lda_model() fails to access gensim HOT 3
- Scopus400Error: Exceeds the maximum number allowed for the service level. HOT 1
- Scopus exceeds csv field limit
- SemanticScholar search optimization HOT 2
- DocumentIdentifier.matches() is case-sensitive
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