Speech and Language Processing. Information Extraction


I 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 historical From Marathon to Waterloo, in order categorical... Imagine that you are an analyst with an investment firm that tracks airline stocks. You're given the task of determining the relationship (if any) between airline announcements of fare increases and the behavior of their stocks the next day. Historical 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 of the proposed fare hike, the dates of the announcement, and possibly the response of other airlines. Fortunately, these can be all found in news articles like this one: Citing high fuel prices, United Airlines said Friday it has increased fares by $6 per round trip on flights to some cities also served by lower-cost carriers. American Airlines, a unit of AMR Corp., immediately matched the move, spokesman Tim Wagner said. United, a unit of UAL 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 content 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 organizations 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 …

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