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View all- Li AFarzan RLópez C(2022)Let’s Work Together! Wikipedia Language Communities’ Attempts to Represent Events WorldwideInteracting with Computers10.1093/iwc/iwac03335:2(69-82)Online publication date: 3-Dec-2022
Wikipedia, rich in entities and events, is an invaluable resource for various knowledge harvesting, extraction and mining tasks. Numerous resources like DBpedia, YAGO and other knowledge bases are based on extracting entity and event based knowledge ...
We automatically create enormous, free and multilingual silver-standard training annotations for named entity recognition (ner) by exploiting the text and structure of Wikipedia. Most ner systems rely on statistical models of annotated data to identify ...
In natural language understanding, extraction of named entity (NE) mentions in given text and classification of the mentions into pre-defined NE types are important processes. Most NE recognition (NER) relies on resources such as a training corpus or NE ...
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