Slicing and Dicing a Linguistic Data Cube

Slicing and Dicing a Linguistic Data Cube

Jan H. Kroeze (University of Pretoria, South Africa)
Copyright: © 2009 |Pages: 13
DOI: 10.4018/978-1-59904-990-8.ch017


This chapter discusses the application of some data warehousing techniques on a data cube of linguistic data. The results of various modules of clausal analysis can be stored in a three-dimensional data cube in order to facilitate on-line analytical processing of data by means of three-dimensional arrays. Slicing is such an analytical technique, which reveals various dimensions of data and their relationships to other dimensions. By using this data warehousing facility the clause cube can be viewed or manipulated to reveal, for example, phrases and clauses, syntactic structures, semantic role frames, or a two-dimensional representation of a particular clause’s multi-dimensional analysis in table format. These functionalities are illustrated by means of the Hebrew text of Genesis 1:1-2:3. The authors trust that this chapter will contribute towards efficient storage and advanced processing of linguistic data.
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Background: Using A Data Cube To Integrate Complex Sets Of Linguistic Data

The clauses constituting a text can be analysed linguistically in various ways depending on the chosen perspective of a specific researcher. These different analytical perspectives regarding a collection of clauses can be integrated into a paper-based medium as a series of two-dimensional tables, where each table represents one clause and its multi-dimensional analysis.

This concept can be explained with a simplified grammatical paradigm and a very small micro-text consisting of only three sentences (e.g. Gen. 1:1a, 4c and 5a)1:

  • Bre$it bara elohim et ha$amayim ve’et ha’arets (in the beginning God created the heaven and the earth)

  • Vayavdel elohim ben ha’or uven haxo$ex (and God separated the light and the darkness)

  • Vayiqra elohim la’or yom (and God called the light day)2

    • An interlinear multi-dimensional analysis of this text can be done as a series of tables (see Table 1).Table 1.

      A series of three two-dimensional tables, each containing a multi-dimensional linguistic analysis of one clause

      Key Terms in this Chapter

      Clause Cube: A clause cube is a three-dimensional data structure that integrates related linguistic data from various language modules.

      Data Warehousing: Data warehousing is the collection, cleaning and reformatting of large amounts of existing data into complex, multi-dimensional data structures, in order to facilitate data mining and exploration (finding patterns and trends hidden within the data). In a data warehouse of linguistic data (e.g. a clause cube) the analyses of various language modules are consolidated in one data structure in order to facilitate the exploration of patterns in and across the interrelated levels.

      Data Cube: A data cube is a multi-dimensional data structure that integrates related data.

      Rotation: Rotation refers to the presentation of various sides or views of a data cube. With reference to a clause cube it refers to the data shown on the “external” sides of the data structure.

      OLAP: On-line analytical processing refers to interactive computer processing to analyse data that has been stored in a database or data warehouse to reveal different and multi-dimensional views (Connolly & Begg, 2005, p. 1205). With reference to a clause cube it pertains to the advanced processing and comparison of linguistic data collected from various language modules.

      Dicing: Dicing may be used as a synonym for rotation, but with reference to a clause cube it refers to the extraction of detailed data “hidden” within the cube.

      Slicing: Slicing refers to the extraction of a subset of data stored in a three-dimensional data cube. With reference to a clause cube a slice may refer, for example, to one clause’s multi-modular analysis represented as a two-dimensional table.

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