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Search results for: TRIPLE PLAY SERVICES
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Elgold partial: Movie reviews
Open Research DataThe dataset contains 37 English texts with movie reviews. In each text, the named entities are marked. Each name entity is linked to the corresponding Wikipedia if possible. All entities were manually verified by at least three people, which makes the dataset a high-quality gold standard for the evaluation of named entity recognition and linking algorithms.
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Elgold partial: Job offers
Open Research DataThe dataset contains 34 English texts scrapped from the web portals offering job offers. In each text, the named entities are marked. Each name entity is linked to the corresponding Wikipedia if possible. All entities were manually verified by at least three people, which makes the dataset a high-quality gold standard for the evaluation of named entity...
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Elgold partial: Scientific papers' abstracts
Open Research DataThe dataset contains 87 Scientific papers' abstracts in English randomly chosen from the folowing scientific disciplines: Biomedicine, Life Sciences, Mathematics, Medicine, Science, Humanities, Social Science.
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Elgold partial: Amazon product reviews
Open Research DataThe dataset contains 34 Amazon product reviews in English. In each text, the named entities are marked. Each name entity is linked to the corresponding Wikipedia if possible. All entities were manually verified by at least three people, which makes the dataset a high-quality gold standard for the evaluation of named entity recognition and linking algorithms.
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Elgold partial: History blogs
Open Research DataThe dataset contains 13 texts from English history blogs. In each text, the named entities are marked. Each name entity is linked to the corresponding Wikipedia if possible. All entities were manually verified by at least three people, which makes the dataset a high-quality gold standard for the evaluation of named entity recognition and linking algorithms.
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SegSperm - a dataset of sperm images for blurry and small object segmentation
Open Research DataMany deep learning applications require figure-ground segmentation. The performance of segmentation models varies across modalities and acquisition settings.