Named-Entity Recognition on News Articles Sample Clauses

Named-Entity Recognition on News Articles. After merging the news articles for predicting events on March 7th into a single file, we utilize NER to extract the nouns of person’s name and organization. The nouns are sorted by frequency, so that only the nouns regularly appear in news reports are kept. The same process is performed for news articles of March 9th. As shown above, besides “Xxxxx” and “CDC” which are two words frequently appear in Covid-related tweets regardless of the occurrence of major events, NER has pulled out many useful information, such as Xxxxxx Xxxxx (Governor of New York), Xxxx xx Xxxxxx (Mayor of New York City), Xxxx Xxxx (Former U.S. Secretary of Health and Human Services), Seema Date Person March 7 Xxxxx, Xxxxxx Xxxxx, Xxxx Xxxx, Xxxx Xxxxx, Xxxxx Xxxxx Organization CDC, Centers for Medicare, International Air Transport Association, Methodist Hospital March 9 Xxxxx, Xxxxxx Xxxxx, xx Xxxxxx, CDC, White House, Columbia University Xxx Xxxxxx Center for Advanced Medicine Table 5.5: NER results of news articles posted on dates marked by Stanford CoreNLP Xxxxx (health policy consultant and former administrator of the Centers for Medicare & Medicaid Services), etc. These people who are frequently discussed in the news are mostly political figures, experts of public health, or a combination of both.
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