semanticscholar | Unofficial Semantic Scholar Academic Graph API client | REST library
kandi X-RAY | semanticscholar Summary
kandi X-RAY | semanticscholar Summary
A python library that aims to retrieve data from Semantic Scholar API.
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Top functions reviewed by kandi - BETA
- Return the raw data for a paper
- Get a single paper
- Get data from url
- Get the author of a given paper
- Return a single author
semanticscholar Key Features
semanticscholar Examples and Code Snippets
Lines of Code : 14License : Permissive (MIT)
Copy>>> from semanticscholar import SemanticScholar
>>> sch = SemanticScholar(timeout=2)
>>> paper = sch.paper('10.1093/mind/lix.236.433')
>>> paper.keys()
dict_keys(['abstract', 'arxivId', 'authors', 'citationVelocity >>> from semanticscholar import SemanticScholar
>>> sch = SemanticScholar(timeout=2)
>>> author = sch.author(2262347)
>>> author.keys()
dict_keys(['aliases', 'authorId', 'citationVelocity', 'influentialCitationCoun >>> from semanticscholar import SemanticScholar
>>> s2_api_key = '40-CharacterPrivateKeyProvidedToPartners'
>>> sch = SemanticScholar(api_key=s2_api_key)
Community Discussions
Trending Discussions on semanticscholar
QUESTION
I've written the code below to get some citation data from an API and write it to a CSV. It works fine except that one of the columns returns a list of authors and it comes into the CSV like this:
[{'authorId': '83129125', 'name': 'June A. Sekera'}, {'authorId': '13328115', 'name': 'A. Lichtenberger'}]
How can I parse this so I get simply a comma-separated list of the authors in a single cell, ignoring the authorId?
...ANSWER
Answered 2022-Mar-27 at 17:29The easiest way I can think of is to use apply(lambda x: ...), creating a list of values for dictionary key "name" in each dictionary p in each item of the column authors.
Add this underneath split_df = pd.DataFrame(...):
QUESTION
I am currently working on a project to build a database on professor's research paper database. This is my first time building a database(never had experience with MYSQL) and I am learning as I am doing it.
I was able to use an api to get the data, for example:
{"authorId": "1773022", "url": "https://www.semanticscholar.org/author/1773022", "papers": [{"paperId": "1253d2704580a74e776ae211602cfde71532c057", "title": "Nonlinear Schrodinger Kernel for hardware acceleration of machine learning"}, {"paperId": "71f49f1e3ccb2e92d606db9b3db66c669a163bb6", "title": "Task-Driven Learning of Spatial Combinations of Visual Features"}, {"paperId": "bb35ae8a50de54c9ca29fbdf1ea2fbbb4e8c4662", "title": "Statistical Learning of Visual Feature Hierarchies"}]}
How would I use python to turn this into a table so I can use it to build my database?
I am trying to make a table where columns are: Paper ID|Title|
...ANSWER
Answered 2022-Feb-17 at 05:40Firstly download MySQL and run the below SQL on the MySQL database to create your MYSQL table
QUESTION
On allennlp textual entailment demo website, the hypothesis and premise in examples always only consist of one sentence. Does allennlp textual entailment model work when hypothesis and premise both include multiple sentences? Is it theoretically practical? Or could I train the model on my own labeled dataset to make it work on paragraph texts?
For example:
- Premise: "Whenever Jack is asked whether he prefers mom or dad, he doesn't know how to respond. To be honest, he has no idea why he has to make a choice. "
- Hypothesis: "Whom do you love more, mom or dad? Some adults like to use this question to tease kids. For Jack, he doesn't like this question."
I read the paper decomposable attention model (Parikh et al, 2017). This paper doesn't discuss such a scenario. The idea behind the paper is text alignment. So intuitively, I think it should also be reasonable to work on paragraph texts. But I'm not very confident about it.
I sincerely appreciate it if anyone can help with it.
...ANSWER
Answered 2021-Jan-08 at 18:51Currently, the datasets for textual entailment (eg. SNLI) contain single sentences as premise and hypothesis. However, the model should still "work" for paragraph texts (as long as the text is within the maximum token limit).
That said, the models trained on these datasets, such as the ones on AllenNLP demo, are likely to have somewhat degraded performance on such inputs, as they have not seen longer examples. In theory, you definitely should be able to train/finetune a model on your own labeled dataset with such examples. One would expect that the performance of the new model would be somewhat improved for longer inputs.
QUESTION
I need to write a fast approx of ln in Python and use the 2.4 algorithm. I know I can get the first a_i numbers with:
ANSWER
Answered 2020-Nov-23 at 14:25If you use a0 and g0 inside the for loop, you make a mistake. You need to initialise arrays with those values.
QUESTION
I have a text file that records metadata of research papers requested with SemanticScholar API. However, when I wrote requested data, I forgot to add "\n" for each individual record. This results in something looks like
ANSWER
Answered 2020-Nov-19 at 17:38The problem is that when you do the split("{") you get a first item that is empty, corresponding to the opening {. Just ignore the first element and everything works fine (I added an r in your quote replacements so python considers then as strings literals and replace them properly):
QUESTION
In chapter 4.7 of Modelica specification 3.4, the definition of the local number of unknowns doesn't count the "across variables", and the Example it gives also implies the same method, but I am not sure why I shouldn't take the "across variables" into consideration?
The "local number of unknowns" isn't the sum of all unknown variables in the model?
I also checked the paper Balanced Models in Modelica 3.0 for Increased Model Quality, here is the screenshot of this paper. The simplest example in this paper to show restriction on physical connectors doesn't tell why it doesn't count the across variables, either.
In my point of view, the local number of unknowns is nf+np, according to Modelica semantics, there are nf equations(m1.c.f = 0; // nf equations) generated by Modelica compiler. So the number of external equations should be ne=nf+np-nf=np. But in this paper, it says that the number of external equations should be ne=nf.
ANSWER
Answered 2020-Oct-05 at 16:45If the connector is unconnected at the next level, this adds nf=ne equations (all flows being zero), and if we instead connect it there will also be nf=ne equations.
Thus if we counted across variables as unknowns locally we would then have to compensate by adding nf or ne equations as well. That would have worked as well, but we decided otherwise - it's likely related to the next point:
A reason for using number of flow variables instead of across variables, is that there are also over-determined connectors where you cannot trivially count the across variables.
QUESTION
In Example 1 from chapter 4.7 of Modelica specification 3.4, I am not sure why there is no structural singularity. I add two equations generated according to the flow variables, and it seems this equation system doesn't have a unique solution.
So why isn't there structural singularity in this example?
I add the two equations(p.i=0 n.i=0) according to the paper Balanced Models in Modelica 3.0 for Increased Model Quality, here is the screenshot of this paper.
How should I understand the concept of "generic coupling" for top-level connectors? Does it mean setting the flow variables as zero or constant?
...ANSWER
Answered 2020-Oct-05 at 16:41As far as I recall the generic coupling means that:
QUESTION
I am trying to get an RC6 implementation from the paper working. I have double checked against the algorithm in the paper and am not sure where I went wrong although my suspicion is key scheduling.
My current output is this:
PLAIN : 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
KEY : 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
ENCRYPT : 30 48 87 4e 00 69 ff 12 da 49 ad 9c 50 8a 0c 96
DECRYPT : 80 53 4a d9 78 b9 37 54 64 8f d4 1d e0 10 56 5d
Trying to achieve the first test vector from the paper:
plaintext 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
user key 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
...ciphertext 8f c3 a5 36 56 b1 f7 78 c1 29 df 4e 98 48 a4 1e
ANSWER
Answered 2020-Aug-15 at 21:28 template static void decrypt(vector& ciphertext, vector& key, unsigned r = 20)
{
vector S(2 * r + 4);
key_schedule(key, S, r);
unsigned w = numeric_limits::digits;
for (auto block = ciphertext.begin(); block != ciphertext.end(); block += 4) {
block[C] -= S[2 * r + 3]; // Removed during refactor
block[A] -= S[2 * r + 2];
...
QUESTION
I'm trying to get the time interval of the last two periods of non-zero demand. The final column should be as shown in nonzero_interval. TIA.
edit: I've added a link to the paper where this question was motivated from.
...ANSWER
Answered 2020-May-25 at 00:49IIUC, you can do it with groupby.transform with count, the groups are created where there are a value not equal to 0 with cumsum. then change where the values are equal to 0 to nan with where, shift and ffill.
QUESTION
I am writing a GSON (Java) parser for the CORD19 dataset https://pages.semanticscholar.org/coronavirus-research of about 40K scientific papers which have been made open for everyone. I want to iterate over the JSON tree using GSON and convert them to HTML. In particular I want to iterate over the entries of the JsonObject elements.
Q1: If anyone has already written an F/OSS CORD19 parser in GSON or other Java parser I'd be delighted.
My specific problem is to iterate over the fields (entries) of a JsonObject.
Data (heavily snipped, but hopefully parsable if snips removed):
...ANSWER
Answered 2020-Mar-23 at 09:59GSON's JsonObject offers the entrySet() method for iterating the contents.
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Install semanticscholar
You can use semanticscholar like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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