Rationalisierung der Suche nach wissenschaftlicher Literatur mit semantischer Ähnlichkeitsanalyse
The aim of this project was to expedite the process of discovering scientific literature on a particular topic, as it is often laborious to sift through and evaluate abstracts to find pertinent ones for research purposes.
Neue mobile Anwendungen, die Sie im Auge behalten sollten
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Welche neuen mobilen Anwendungen für soziale Medien werden 2022 verfügbar sein?
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Neue Social-Media-Anwendungen als Marketingtrichter nutzen
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Probieren Sie Twitter Spaces oder Clubhouse auf dem iPhone aus
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Mit welcher Anwendung experimentieren Sie gerade?
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Rationalisierung der Suche nach wissenschaftlicher Literatur mit semantischer Ähnlichkeitsanalyse
The goal of this project was to streamline the process of discovering scientific literature related to a particular topic, as the task of sifting through and evaluating abstracts can be time-consuming and laborious for researchers. To expedite this process, the project utilized the arXiv API to retrieve results matching specific keywords. The retrieved results were then compared using semantic similarity analysis to identify relevant literature quickly and efficiently. This method saves researchers a significant amount of time by eliminating the need to read through numerous abstracts to identify the most pertinent ones for their research. The project's outcome offers a valuable tool for researchers to compare existing literature in a specific field and identify gaps that need further investigation.
Welche Daten wir verwendet haben
24'546 scientific papers from arXiv on the topic of «Semantic Search»
Zielsetzungen
— Finding patterns and similarities in scientific papers
— Gaining more insights about sub-fields topic modelling
— Getting recommendations for similar papers
Liefergegenstände
— 2D Interface with cluster and search
— Topics from topic modelling
— Recommender system
