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Networks of Keyness and Key Key Words for Document Retrieval

This presentation discusses the network patterns of keyness in texts, exploring how they relate to user needs and interests. Key words, aboutness, and distribution patterns of key words are analyzed, taking into account the importance and fractal nature of key key words. The presentation also delves into machine-identified keyness for corpus-driven research, addressing the dispersion of global and local key words within the text. The concept of key key words as associates within a corpus is explored, along with the measurement of association strength using collocation statistics.

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Networks of Keyness and Key Key Words for Document Retrieval

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  1. Networks ofKey Words Mike Scott Aston University INWWCT, Trondheim October 3rd, 2011

  2. Abstract The notion of keyness is important for document retrieval, for language learning and for study of the nature of text. Keyness, a textual not a linguistic quality, may be shared by certain words and phrases in one text, but its patterning is further distributed across text sets of various dimensions in associates (Scott, 1997) and clustering. This presentation considers the network patterns of keyness which can be investigated using quite simple software procedures and the extent to which these patternings may relate to a user’s needs and interests. Scott, M., 1997, "PC Analysis of Key Words -- and Key Key Words", System, Vol. 25, No. 1, pp. 1-13.

  3. Keyness • Aboutness • Distribution patterns of KWs • … in texts and across corpora Key words (KWs) Issues

  4. complex pattern

  5. or simple

  6. fractal?

  7. A fractal is "a rough or fragmented geometric shape that can be split into parts, each of which is (at least approximately) a reduced-size copy of the whole,"[1] a property called self-similarity • (Wikipedia) • [1] Mandelbrot, B.B. (1982). The Fractal Geometry of Nature. W.H. Freeman and Company. Fractal

  8. aboutness • importance • a textual category Keyness

  9. KWs

  10. frequencies

  11. what the text is about • what the message is • what it all means • picture from mindreadersdictionary.com aboutness

  12. importance centrality

  13. simple verbatim repetition • no allowance for anaphora, synonymy, antonymy etc. • simple frequency threshold • one word, or more than one? PC Identificationof KWs

  14. Machine-identified keyness is ideal for corpus-driven research • The researcher lets the PC suggest areas needing further chasing up • See recent work by McEnery, Baker, etc. Corpus-based or corpus-driven?

  15. Dispersion within the text

  16. Global KWs

  17. Local KWs

  18. verbs appears begins puts observes replies continues says considers etc. middling burstiness

  19. Distribution patterns across the corpus

  20. A "key key-word" is one which is "key" in more than one of a number of related texts. • The more texts it is "key" in, the more "key key" it is. Key Key Words

  21. An "associate" of key-word X is another key-word (Y) which co-occurs with X in a number of texts. • (It may or may not co-occur in proximity to key-word X.) • Association strength measured using a standard collocation statistic, here MI3 Associates

  22. LexisNexis database • 9,444 stories • UK press • 2010 • “climate change” Climate change

  23. KKWs

  24. Associates

  25. waste

  26. university

  27. KW patterns within individual texts • within the corpus or sub-corpus • but early days, lots of questions: • are any KW patternings fractal? • do specialised corpora have specialised KKWs? Conclusions

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