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6 results for “idioms”
EMOTIVE IDIOMS
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Does Coding in Pythonic Zen Peak Performance? Preliminary Experiments of Nine Pythonic Idioms at Scale
<p><strong>Link to pre-print: </strong><a href="https://arxiv.org/abs/2203.14484">https://arxiv.org/abs/2203.14484</a><br> <br> <strong>How to run</strong></p> <ol> <li>Extract <strong>pythonnic_performance.zip </strong>and move into the extracted directory.</li> <li>Execute <strong>install.sh</strong> to install required dependencies.</li> <li>Execute <strong>run.sh </strong>to start the experiment.</li> <li>Execute <strong>python statistic.py </strong>to perform the statistical test and show data statistics.</li> </ol> <p>Note that files in <strong>submitted_output</strong> are our experimental results shown in the paper.</p> <p><br> <strong>Abstract</strong></p> <p>In field of data science, and for academics in general, the Python programming language is a popular choice, mainly because of its libraries for storing, manipulating, and gaining insight from data.<br> Evidence includes the versatile set of machine learning, data visualization, and manipulation packages used for the ever-growing size of available data.<br> The <em>Zen of Python</em> is a set of guiding design principles that developers use to write acceptable and elegant Python code.<br> Most principles revolve around simplicity.<br> However, as the need to compute on large amounts of data, performance has become a necessity for the Python programmer.</p> <p>The new idea in this paper is to empirically confirm whether writing the Pythonic way peaks performance at scale.<br> As a starting point, we conduct a set of preliminary experiments to evaluate nine Pythonic code examples by comparing the performance of both Pythonic and Non-Pythonic code snippets.<br> Our results reveal that writing in Pythonic idioms may save memory and time.<br> We show that incorporating list comprehension, generator expression, zip, and itertools.zip\_longest can save up to 7,000 MB and 32.25 seconds.<br> The results open more questions on how they could be utilized in a real-world setting.</p>
Data of the Shared Task on the Disambiguation of German Verbal Idioms at KONVENS 2021
<p>This dataset was used in the Shared Task on the Disambiguation of German Verbal Idioms (VID) at <a href="https://konvens2021.phil.hhu.de/">KONVENS 2021</a>. For further details, please refer to the description paper of the shared task:</p> <blockquote> <p>Ehren, Rafael, Timm Lichte, Jakub Waszczuk & Laura Kallmeyer. 2021. Shared Task on the Disambiguation of German Verbal Idioms at KONVENS 2021. In Proceedings of the Shared Task on the Disambiguation of German Verbal Idioms at KONVENS 2021. <a href="https://doi.org/10.5281/zenodo.5730322">https://doi.org/10.5281/zenodo.5730322</a>. <a href="https://konvens.org/proceedings/2021/index.html">https://konvens.org/proceedings/2021/index.html</a>.</p> </blockquote> <p><strong>Please cite this paper when using the dataset.</strong></p> <p>The content of the zip file is identical to that of the data directory in the <a href="https://github.com/rafehr/vid-disambiguation-sharedtask/tree/3ceb0bb423fa73e70ac018d4e02063ae449b4542">Github repository of the shared task</a>.</p> <p>The dataset consists of 9901 instances of a German VID type or its literal counterpart in context. The set of VID types was pre-selected, thus it constitutes a lexical sample data set. It is a merger of two datasets:</p> <ul> <li><a href="https://www.aclweb.org/anthology/2020.figlang-1.29.pdf">COLF-VID</a> (instances with T*)</li> <li><a href="https://www.aclweb.org/anthology/S13-2007.pdf">German SemEval-2013 task 5b</a> (instances with S*)</li> </ul> <p>The data comes in tsv files and every line has the following format:</p> <pre><code>Instance_ID \t VID_type \t label \t text</code></pre> <p>Consider this example:</p> <pre><code>T890202.28.4077 in wasser fallen figuratively Der Streit ums Hormonfleisch zwischen USA und EG provozierte den Polizeieinsatz . Aber nicht nur der Steakverkauf , auch die Aktionen gegen den Hormonstand , auf die sich Gruppen der Bauernopposition schon vorbereitet hatten , <b>fielen</b> <b>ins</b> <b>Wasser</b> . Die Fleischexporteure der USA wollten ihrerseits die " Grüne Woche " zur " Aufklärung " nutzen .</code></pre> <p>So the first column contains the ID (T890202.28.4077 in the example), the second the VID type (in wasser fallen), the third the label (figuratively) and the fourth the sentence with either the instance of the VID type or its literal counterpart (and two additional context sentences). The parts of the target expression are marked with the <b> tag (<b>fielen</b> <b>ins</b> <b>Wasser</b>). There are four possible labels:</p> <ul> <li>figuratively</li> <li>literally</li> <li>undecidable</li> <li>both</li> </ul> <p>The first two should be self-explanatory. The label undecidable was used by the annotators if it was not possible to disambiguate an instance given the context. The label both was applied when both the literal and the idiomatic readings were active.</p>
A Mechanism for Automatically Extracting Reusable and Maintainable Code Idioms from Software Repositories
<p>The provided dataset contains the data used by "A Mechanism for Automatically Extracting Reusable and Maintainable Code Idioms from Software Repositories", in order to extract maintainable and reusable code idioms from the most popular GitHub repositories. The dataset includes the code snippets, the Abstract Syntax Trees and repositories information about Control Flow Statements (if-blocks, for-blocks, enhanced-for-blocks, try-blocks, while-blocks, switch-blocks and do-blocks) data coming from the top GitHub projects.</p>
Dataset and tool - A Static Analysis for Discovering Type-Related Coding Idioms in Python Programs
<p>The archive file contains the following materials:</p> <ul> <li>`repos.tar.gz`: dataset of the collected Python repositories</li> <li>`tool.tar.gz`: programs used in the case studies</li> </ul>
Artifact: Coding Assistance with Interlanguage Translation of Idioms by LLM
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