1,720,955 research outputs found
Word Importance Dataset
No full textThis dataset comprises a corpus of 50 text contexts, each about 60 words in length, sourced from five distinct domains. Each context has been evaluated by multiple annotators who identified and ranked the most important words—up to 10% of each text—according to their perceived significance. The annotators followed specific guidelines to ensure consistency in word selection and ranking. For further details, please refer to the cited source.
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rankings_task.csv
- This csv contains information about the contexts which are to be annotated:
- id: A unique identifier for each task.
- content: The context to be ranked.
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rankings_ranking.csv
- This csv includes ranking information for various assignments. It contains four columns:
- id: A unique identifier for each ranking entry.
- score: The score assigned to the entry.
- word_order: A JSON detailing the order of words positions. It is essentially the selected word positions and their ordering from an annotator.
- assignment_id: A reference ID linking to the assignments.
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rankings_assignment.csv
- This csv tracks the completion status of tasks by users. It includes four columns:
- id: A unique identifier for each assignment entry.
- is_completed: A binary indicator (1 for completed, 0 for not completed).
- task_id: A reference ID linking to the tasks.
- user_id: The identifier for the user who should complete the task (rank the words).
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Known Issues:
Please note that each annotator was intended to rank each context only once. However, due to a bug in the deployment of the annotation tool, some entries may be duplicated. Users of this dataset should be cautious of this issue and verify the uniqueness of the annotations where necessary.
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This dataset is a part of work from a bachelor thesis:
OSUSKÝ, Adam. Predicting Word Importance Using Pre-Trained Language Models. Bachelor thesis, supervisor Javorský, Dávid. Prague: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics, 2024
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Predikcia dôležitosti slov pomocou predtrénovaných jazykových modelov
Full text linkTato bakalářská práce komplexně zkoumá hodnocení důležitosti slov, od definování tohoto pojmu po vytvoření a vyhodnocení predikčního systému. Po- mocí našeho webového anotačního nástroje jsme sebrali ruční odhady důležitosti slov; důležitost přitom definujeme jako relativní uspořádání slov. Navrhujeme metodu self-supervised strojového učení, kde jsou do textu uměle vložena nová slova a my pak dolaďujeme model BERT, aby se naučil tato slova identifikovat. Předpokládáme, že výsledný model přidělí vyšší pravděpodobnost vložení méně důležitým slovům. Experimentujeme se dvěma různými strategiemi vkládání: metodou vkládání seznamem a metodou vkládání BERTem. Vyhodnocení na našich shromážděných datech ukazuje, že naše metody překonávají tradiční základní metody jako TF-IDF a soupeří s existujícími přístupy, což dokládá funkčnost našeho přístupu při predikci důležitosti slov. 1This thesis explores the assessment of word importance, from defining the concept to creating and evaluating a prediction system. We collect word impor- tance labels using our web-based annotation tool and define word importance as word rankings. We propose a self-supervised machine learning method where new words are artificially inserted into text, and then we fine-tune the BERT model to learn to identify these words. We hypothesize that the resulting model will assign a higher likelihood of insertion to less important words. We exper- iment with two different insertion strategies: the List Inserting Method and the BERT Inserting Method. Evaluations on our collected data show that our methods outperform traditional baselines such as TF-IDF and rival existing ap- proaches, demonstrating the effectiveness of our approach in predicting word importance. 1Institute of Formal and Applied LinguisticsÚstav formální a aplikované lingvistikyMatematicko-fyzikální fakultaFaculty of Mathematics and Physic
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Predicting Word Importance Using Pre-Trained Language Models
No full textThis thesis explores the assessment of word importance, from defining the concept to creating and evaluating a prediction system. We collect word impor- tance labels using our web-based annotation tool and define word importance as word rankings. We propose a self-supervised machine learning method where new words are artificially inserted into text, and then we fine-tune the BERT model to learn to identify these words. We hypothesize that the resulting model will assign a higher likelihood of insertion to less important words. We exper- iment with two different insertion strategies: the List Inserting Method and the BERT Inserting Method. Evaluations on our collected data show that our methods outperform traditional baselines such as TF-IDF and rival existing ap- proaches, demonstrating the effectiveness of our approach in predicting word importance.
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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