Transcription of CHAPTER 17 Information Extraction - Stanford University
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CHAPTER 17 Information Extraction - Stanford University
Speech and Language Processing. Daniel Jurafsky & James H. Martin. Copyright 2021. Allrights reserved. Draft of December 29, ExtractionI am the very model of a modern Major-General,I ve Information vegetable, animal, and mineral,I know the kings of England, and I quote the fights historicalFrom Marathon to Waterloo, in order and Sullivan,Pirates of PenzanceImagine that you are an analyst with an investment firm that tracks airline re given the task of determining the relationship (if any) between airline an-nouncements of fare increases and the behavior of their stocks the next day. His-torical data about stock prices is easy to come by, but what about the airline an-nouncements? You will need to know at least the name of the airline, the nature ofthe proposed fare hike, the dates of the announcement, and possibly the response ofother airlines.
For example the crowdsourced DBpedia (Bizer et al.,2009) is an ontology de-rived from Wikipedia containing over 2 billion RDF triples. Another dataset from Freebase Wikipedia infoboxes, Freebase (Bollacker et al.,2008), now part of Wikidata (Vrandeciˇ c´ and Krotzsch¨ ,2014), has relations between people and their nationality, or locations,
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