jurafsky&martin_3rdEd_17 (1).pdf

Said the increase took effect thursday and applies to

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Corp., said the increase took effect Thursday and applies to most routes where it competes against discount carriers, such as Chicago to Dallas and Denver to San Francisco. This chapter presents techniques for extracting limited kinds of semantic con- tent from text. This process of information extraction (IE), turns the unstructured information extraction information embedded in texts into structured data, for example for populating a relational database to enable further processing. The first step in most IE tasks is to find the proper names or named entities mentioned in a text. The task of named entity recognition (NER) is to find each named entity recognition mention of a named entity in the text and label its type. What constitutes a named entity type is application specific; these commonly include people, places, and or- ganizations but also more specific entities from the names of genes and proteins (Cohen and Demner-Fushman, 2014) to the names of college courses (McCallum, 2005) . Having located all of the mentions of named entities in a text, it is useful to link, or cluster, these mentions into sets that correspond to the entities behind the mentions, for example inferring that mentions of United Airlines and United in the sample text refer to the same real-world entity. We’ll defer discussion of this task of coreference resolution until Chapter 23. The task of relation extraction is to find and classify semantic relations among relation extraction the text entities, often binary relations like spouse-of, child-of, employment, part- whole, membership, and geospatial relations. Relation extraction has close links to populating a relational database.
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21.1 N AMED E NTITY R ECOGNITION 349 The task of event extraction is to find events in which these entities participate, event extraction like, in our sample text, the fare increases by United and American and the reporting events said and cite . We’ll also need to perform event coreference to figure out which of the many event mentions in a text refer to the same event; in our running example the two instances of increase and the phrase the move all refer to the same event. To figure out when the events in a text happened we’ll do recognition of tem- poral expressions like days of the week ( Friday and Thursday ), months, holidays, temporal expression etc., relative expressions like two days from now or next year and times such as 3:30 P.M. or noon . The problem of temporal expression normalization is to map these temporal expressions onto specific calendar dates or times of day to situate events in time. In our sample task, this will allow us to link Friday to the time of United’s announcement, and Thursday to the previous day’s fare increase, and produce a timeline in which United’s announcement follows the fare increase and American’s announcement follows both of those events.
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