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10 results for “Search Query”
The language of sound search: Examining User Queries in Audio Search Engines (supplementary materials)
<h2>Overview</h2> <p>This dataset accompanies the <a href="https://dcase.community/documents/workshop2024/proceedings/DCASE2024Workshop_Weck_54.pdf" target="_blank" rel="noopener">paper</a> titled <strong>"The Language of Sound Search: Examining User Queries in Audio Search Engines."</strong> The study investigates user-generated textual queries within the context of sound search engines, which are commonly used for applications such as foley, sound effects, and general audio retrieval.</p> <p>The paper addresses the gap in current research regarding the real-world needs and behaviors of users when designing text-based audio retrieval systems. By analyzing search queries collected from two sources — a custom survey and Freesound query logs — the study provides insights into user behavior in sound search contexts. Our findings reveal that users tend to formulate longer and more detailed queries when not constrained by existing systems, and that both survey and <a href="https://freesound.org/">Freesound</a> queries are predominantly keyword-based.</p> <p>This dataset contains the raw data collected from the survey and annotations of Freesound query logs.</p> <h2>Files in This Dataset</h2> <p>The dataset includes the following files:</p> <ol> <li> <p><strong><code>participants.csv</code></strong><br>Contains data from the survey participants. Columns:</p> <ul> <li><code>id</code>: A unique identifier for each participant.</li> <li><code>fluency</code>: Self-reported English language proficiency.</li> <li><code>experience</code>: Whether the participant has used online sound libraries before.</li> <li><code>passed_instructions</code>: Boolean value indicating whether the participant advanced past the instructions page in the survey.</li> </ul> </li> <li> <p><strong><code>annotations.csv</code></strong><br>Contains annotations of the survey responses, detailing the participants' interaction with the sound search tasks. Columns:</p> <ul> <li><code>id</code>: A unique identifier for each annotation.</li> <li><code>participant_id</code>: Links to the participant’s ID in <code>participants.csv</code>.</li> <li><code>stimulus_id</code>: Identifier for the stimulus presented to the participant (audio, image, or text description).</li> <li><code>stimulus_type</code>: The type of stimulus (audio, image, text).</li> <li><code>audio_result_id</code>: Identifier for the hypothetical audio result presented during the search task.</li> <li><code>query1</code>: Initial search query submitted based on the stimulus.</li> <li><code>query2</code>: Refined search query after seeing the hypothetical search result.</li> <li><code>aspects1</code>: Aspects considered important when formulating the initial query.</li> <li><code>aspects2</code>: Aspects considered important when refining the query.</li> <li><code>result_relevance</code>: Participant's rating of the hypothetical search result's relevance.</li> <li><code>time</code>: Time taken to complete the search task.</li> </ul> </li> <li> <p><strong><code>freesound_queries_annotated.csv</code></strong><br>Contains annotated Freesound search queries. Columns:</p> <ul> <li><code>query</code>: Text of the search query submitted to Freesound.</li> <li><code>count</code>: The number of times the specific query was submitted.</li> <li><code>topic</code>: Annotated topic of the query, based on an ontology derived from AudioSet, with an additional category, <code>Other</code>, which includes non-English queries and NSFW-related content.</li> </ul> </li> <li> <p><strong><code>survey_stimuli_data.zip</code></strong><br>This ZIP file contains three CSV files corresponding to the three stimulus types used in the survey:</p> <ul> <li><strong>Audio stimuli</strong>: Categorized sound recordings presented to participants.</li> <li><strong>Image stimuli</strong>: Annotated images that prompted sound-related queries.</li> <li><strong>Text stimuli</strong>: Summarized descriptions of sounds provided to participants.</li> </ul> </li> </ol> <p>More details on the stimuli and the survey methodology can be found in the accompanying paper.</p> <h2><strong>Citation</strong></h2> <p>If you use this dataset in your research, please cite the corresponding paper:</p> <div> <pre>B. Weck and F. Font, ‘The Language of Sound Search: Examining User Queries in Audio Search Engines’, in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2024 Workshop (DCASE2024), Tokyo, Japan, Oct. 2024, pp. 181–185.</pre> <pre><code>@inproceedings{Weck2024, author = "Weck, Benno and Font, Frederic", title = "The Language of Sound Search: Examining User Queries in Audio Search Engines", booktitle = "Proceedings of the Detection and Classification of Acoustic Scenes and Events 2024 Workshop (DCASE2024)", address = "Tokyo, Japan", month = "October", year = "2024", pages = "181--185" }</code></pre> </div>
Qbias – A Dataset on Media Bias in Search Queries and Query Suggestions
<p>We present Qbias, two novel datasets that promote the investigation of bias in online news search as described in</p> <blockquote> <p>Fabian Haak and Philipp Schaer. 2023. 𝑄𝑏𝑖𝑎𝑠 - A Dataset on Media Bias in Search Queries and Query Suggestions. In Proceedings of ACM Web Science Conference (WebSci’23). ACM, New York, NY, USA, 6 pages. <a href="https://doi.org/10.1145/3578503.3583628">https://doi.org/10.1145/3578503.3583628</a>.</p> </blockquote> <p><strong>Dataset 1: AllSides Balanced News Dataset (allsides_balanced_news_headlines-texts.csv)</strong></p> <p>The dataset contains 21,747 news articles collected from <a href="https://www.allsides.com/headline-roundups">AllSides balanced news headline</a> roundups in November 2022 as presented in our publication. The AllSides balanced news feature three expert-selected U.S. news articles from sources of different political views (left, right, center), often featuring spin bias, and slant other forms of non-neutral reporting on political news. All articles are tagged with a bias label by four expert annotators based on the expressed political partisanship, left, right, or neutral. The AllSides balanced news aims to offer multiple political perspectives on important news stories, educate users on biases, and provide multiple viewpoints. Collected data further includes headlines, dates, news texts, topic tags (e.g., "Republican party", "coronavirus", "federal jobs"), and the publishing news outlet. We also include AllSides' neutral description of the topic of the articles.<br> Overall, the dataset contains 10,273 articles tagged as left, 7,222 as right, and 4,252 as center.</p> <p>To provide easier access to the most recent and complete version of the dataset for future research, we provide a scraping tool and a regularly updated version of the dataset at <a href="https://github.com/irgroup/Qbias">https://github.com/irgroup/Qbias</a>. The repository also contains regularly updated more recent versions of the dataset with additional tags (such as the URL to the article). We chose to publish the version used for fine-tuning the models on Zenodo to enable the reproduction of the results of our study. </p> <p> </p> <p><strong>Dataset 2: Search Query Suggestions (suggestions.csv)</strong></p> <p>The second dataset we provide consists of 671,669 search query suggestions for root queries based on tags of the AllSides biased news dataset. We collected search query suggestions from Google and Bing for the 1,431 topic tags, that have been used for tagging AllSides news at least five times, approximately half of the total number of topics. The topic tags include names, a wide range of political terms, agendas, and topics (e.g., "communism", "libertarian party", "same-sex marriage"), cultural and religious terms (e.g., "Ramadan", "pope Francis"), locations and other news-relevant terms. On average, the dataset contains 469 search queries for each topic. In total, 318,185 suggestions have been retrieved from Google and 353,484 from Bing.</p> <p>The file contains a "root_term" column based on the AllSides topic tags. The "query_input" column contains the search term submitted to the search engine ("search_engine"). "query_suggestion" and "rank" represents the search query suggestions at the respective positions returned by the search engines at the given time of search "datetime". We scraped our data from a US server saved in "location".</p> <p>We retrieved ten search query suggestions provided by the Google and Bing search autocomplete systems for the input of each of these root queries, without performing a search. Furthermore, we extended the root queries by the letters a to z (e.g., "democrats" (root term) >> "democrats a" (query input) >> "democrats and recession" (query suggestion)) to simulate a user's input during information search and generate a total of up to 270 query suggestions per topic and search engine. The dataset we provide contains columns for root term, query input, and query suggestion for each suggested query. The location from which the search is performed is the location of the Google servers running Colab, in our case Iowa in the United States of America, which is added to the dataset. </p> <p><strong>AllSides Scraper</strong></p> <p>At <a href="https://github.com/irgroup/Qbias">https://github.com/irgroup/Qbias</a>, we provide a scraping tool, that allows for the automatic retrieval of all available articles at the AllSides balanced news headlines. </p> <p>We want to provide an easy means of retrieving the news and all corresponding information. For many tasks it is relevant to have the most recent documents available. Thus, we provide this Python-based scraper, that scrapes all available AllSides news articles and gathers available information. By providing the scraper we facilitate access to a recent version of the dataset for other researchers.</p> <p> </p>
Summon Topic Explorer Results by Search Query
<p>8,000 Summon Topic Explorer results from actual user search queries at GVSU using Summon 2.0 with the Topic Explorer fed from Wikipedia and Gale Virtual Reference Library.</p> <p>topics.xlsx is a collection of the 8,000 analyzed search queries with Topic Explorer results.</p> <p>All_Problematic_Searches.xlsx is a sheet of 561 incorrect or biased Topic Explorer results.</p> <p>Results_with_Bias.xlsx contains 54 biased Topic Explorer results.</p>
Webis-Context-sensitive-Word-Search-Queries-2022
<pre>This is the dataset created for Language Models as Context-sensitive Word Search Engines at the In2Writing workshop at ACL22. </pre> <p> </p> <p><strong>Cite</strong> </p> <pre><code>@inproceedings{wiegmann:2022, title = "Language Models as Context-sensitive Word Search Engines", author = "Wiegmann, Matti and V{\"{o}}lske, Michael and Potthast, Martin and Stein, Benno", booktitle = "Proceedings of the 1st Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2022)", month = may, year = "2022", address = "Online", publisher = "Association for Computational Linguistics",</code></pre> <p><strong>Datasets</strong></p> <p>This repository contains two datasets with word search queries. Each word search query consists of a token n-gram with one wildcard token ([MASK]). The answers to each query are the most likely token to replace the mask. All queries originate from wikitext-103 and CLOTH, the respected source is annotated for each query.</p> <p>The <em>original-token</em> dataset lists exactly one top answer for each query. The <em>ranked-answers</em> dataset lists multiple, sorted answers in three relevance categories, where 3 is the most relevant. Please refer to the citation for more details.</p>
Bird predation on Roseau cane scale as revealed by a web image search and querying a citizen monitoring database
<p>NA</p>
Supplementary Material for Nested Segmentation of Web Search Queries
<p>1. Query test of SGCL12 [SGCL12QueryTestSet.txt]</p> <p>2. Outputs of 16 nesting algorithms on the query test set of SGCL12 [Input flat segmention: Saha Roy et al., SIGIR 2012] [Nested Segmentation Outputs SGCL12.zip]</p> <p>3. Query set of TREC-WT [TREC-WTQueryTestSet.txt]</p> <p>4. Outputs of 16 nesting algorithms on the query test set of TREC-WT [Input flat segmention: Saha Roy et al., SIGIR 2012] [Nested Segmentation Outputs TREC-WT.zip]</p> <p>5. Code and executables for generating the nested segmentations for a set of queries [Code and execs for generating nested segmentations.zip]</p> <p>6. Code and executables for IR-based evaluation of a nested segmentation [Code and execs for IR evaluation of a nested segmentation.zip]</p> <p>7. Readme.txt</p>
Search-Based Test Data Generation for SQL Queries: Appendix
<p>The appendix of our ICSE 2018 paper "Search-Based Test Data Generation for SQL Queries: Appendix".</p> <p>The appendix contains:</p> <ul> <li>The queries from the three open source systems we used in the evaluation of our tool (the industry software system is not part of this appendix, due to privacy reasons)</li> <li>The results of our evaluation.</li> <li>The source code of the tool. Most recent version can be found at https://github.com/SERG-Delft/evosql.</li> <li>The results of the tuning procedure we conducted before running the final evaluation.</li> </ul>
Data from: Bird predation on Roseau cane scale as revealed by a web image search and querying a citizen monitoring database
Open the record for dataset details and reuse information.
Stroke Prediction Through Internet Search Queries
ClinicalTrials.gov study NCT04755959. IPD Sharing: UNDECIDED. Countries: 0. Publications: 4.
Query structures from Figure 4 of Comparison of Substructure Search Systems
<p>These 29 SMARTS queries come from Figure 4 on page 193 of:</p> <p> Martin G. Hicks and Clemens Jochum,<br> Substructure search systems. 1. Performance comparison of the<br> MACCS, DARC, HTSS, CAS Registry MVSSS, and S4 substructure search systems,<br> Journal of Chemical Information and Computer Sciences 1990 30 (2), 191-199<br> <a href="https://doi.org/10.1021/ci00066a018">https://doi.org/10.1021/ci00066a018</a></p> <p>The SMARTS queries are in Figure 4 (page 193). A copy of the original table is in 10.1021_ci00066a018_Figure_4.pdf . The LibreOffice spreadsheet file 10.1021_ci00066a018.ods contain two sheets. The "Overview" sheet gives a summary of the data set. The "Query structures" sheet contains the data from Figure 4.</p> <p>The Hicks and Jochum paper does not give the query as SMARTS strings. Instead, the queries were translated to SMARTS in the following paper:</p> <p> Dimitris K. Agrafiotis, Victor S. Lobanov, Maxim Shemanarev,<br> Dmitrii N. Rassokhin, Sergei Izrailev, Edward P. Jaeger,<br> Simson Alex, and Michael Farnum<br> Efficient Substructure Searching of Large Chemical Libraries:<br> The ABCD Chemical Cartridge<br> Journal of Chemical Information and Modeling 2011 51 (12), 3113-3130<br> <a href="https://doi.org/10.1021/ci200413e">https://doi.org/10.1021/ci200413e</a></p> <p>Two of the SMARTS patterns were changed to produce this dataset. During cross-checking with the original paper, and with the help of SMARTSviewer, I believe I identified two mistakes in the conversion, described in the README.</p>
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