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Information extraction (IE) is a technology based on analysing natural language in order to extract snippets of information. The process takes texts (and sometimes speech) as input and produces xed format, unambiguous data as output. This data may be used directly for display to users, or may be stored in a database or spreadsheet for later analysis, or may be used for indexing purposes in information retrieval (IR) applications such as internet search engines like Google. IE is quite different from IR: an IR system nds relevant texts and presents them to the user; an IE application analyses texts and present only the speci c information from them that the user is interested in. For example, a user of an IR system wanting information on trade group formations in agricultural commodities markets would enter a list of relevant words and receive in return a set of documents (e.g., newspaper articles) which contain likely matches. The user would then read the



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While perfect streaming load balance is desirable, the cost of data reorganization, which itself consumes system resources, can still be substantial. Depending on the particular system con guration (e.g., disk throughput, network bandwidth, system utilization, etc.), it may be desirable to trade off some streaming load balance to further reduce the data reorganization overhead. By relaxing the streaming load balance constraint, we generalize the row-permutated data reorganization algorithm into a multi-row-permutated data reorganization (m-RPDR) algorithm, which also subsumes the original RPDR as the special case 1-RPDR. In m-RPDR we process the media blocks m groups at a time. Moreover, we rede ne node over ow to occur only if more than m blocks from the m groups are stored in the same node. Similarly, a node under ows if it stores fewer than m blocks from the m groups under consideration. Figure 15.7 illustrates the reorganization of the rst two groups using 2-RPDR. In this example, nodes n 0 and n 2 over ow and so we move media data blocks v1,3 and v1,4 to n 4 to restore streaming load balance. Comparing to reorganizing the same two groups using 1-RPDR (Figure 15.4 and Figure 15.5), the number of block movements is reduced from 3 to 2. The trade-off is in streaming load balance. As shown in Figure 15.7 node n 4 stores media data blocks v1,3 and v1,4 , and node n 1 stores blocks v0,1 and v0,4 . Thus, if the system sends a group of N data blocks in each service round, then in the rst service round node n 1 will need to send two data blocks while node n 4 will be idle, and vice versa in the second service round.





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n0, n2 overflow while n4 underflows. v0,0 v1,2 v1,3 v2,3 v3,1 . . . n0 v0,1 v0,4 v2,0 v2,2 v3,2 . . . n1 v0,3 v1,0 v1,4 v2,4 v3,4 . . . n2 v0,2 v1,1 v2,1 v3,0 v3,3 . . . n3 n4 Re-organize

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documents and extract the requisite information themselves. They might then enter the information in a spreadsheet and produce a chart for a report or presentation. In contrast, an IE system would automatically populate the spreadsheet directly with the names of relevant companies and their groupings. There are advantages and disadvantages with IE in comparison to IR. IE systems are more dif cult and knowledge-intensive to build, and are to varying degrees tied to particular domains and scenarios. IE is more computationally intensive than IR. However, in applications where there are large text volumes IE is potentially much more ef cient than IR because of the possibility of dramatically reducing the amount of time people spend reading texts. Also, where results need to be presented in several languages, the xed-format, unambiguous nature of IE results makes this relatively straightforward in comparison with providing the full translation facilities needed for interpretation of multilingual texts found by IR. Useful overview sources for further details on IE include: Cowie and Lehnert (1996), Appelt (1999), Cunningham (2005), Gaizauskas and Wilks (1998) and Pazienza (2003).

Blocks v1,3 and v1,4 are moved to n4. v0,0 v1,2 v0,1 v0,4 v2,0 v2,3 v3,1 . . . n0 v2,2 v3,2 . . . n1 v2,4 v3,4 . . . n2 v0,3 v1,0 v0,2 v1,1 v2,1 v3,0 v3,3 . . . n3 n4 v1,3 v1,4

IE is about nding ve different types of information in natural language text: 1. Entities: things in the text, for example people, places, organisations, amounts of money, dates, etc. 2. Mentions: all the places that particular entities are referred to in the text. 3. Descriptions of the entities present. 4. Relations between entities. 5. Events involving the entities. For example, consider the text:

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