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There are reasons for positing a word structure

Maybe in order to understand mankind, we have to look at the word itself: "Mankind". Basically, it's made up of two separate words - "mank" and "ind". What do these words mean? It's a mystery, and that's why so is mankind. Jack Handy. There are reasons for positing a word structure.

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There are reasons for positing a word structure

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  1. Maybe in order to understand mankind, we have to look at the word itself: "Mankind". Basically, it's made up of two separate words - "mank" and "ind". What do these words mean? It's a mystery, and that's why so is mankind. Jack Handy

  2. There are reasons for positing a word structure

  3. There are at least three conditions on structuring a word w into x.y

  4. There are at least three conditions on structuring w into x.y x is a stem and y is a suffix

  5. There are at least three conditions on structuring w into x.y x is a stem and y is a suffix y selects x

  6. There are at least three conditions on structuring w into x.y x is a stem and y is a suffix y selects x x and y are relevant for the distribution of w

  7. Arguments for x being a stem carries over to an argument that y is a suffix

  8. If x is a stem then x has meaning

  9. If x is a stem then x has meaning stem(x) → meaning(x)

  10. If x is a stem then x has meaning stem(x) → meaning(x) word(x) → meaning(x)

  11. Being a stem is translated into being a word

  12. Being a stem is translated into being a word Pr( stem(x)| w=x.y) ~ Pr(word(x)| w=x.y)

  13. Being a stem is translated into being a word Pr( stem(x)| w=x.y) ~ Pr(word(x)| w=x.y)

  14. Being a stem is translated into being a word Pr( stem(x)| w=x.y) ~ Pr(word(x)| w=x.y)

  15. A beta distribution is used for assigning a probability based on the proportion

  16. A beta distribution is used for assigning a probability based on the proportion beta(positive, negative)

  17. The top ten list

  18. The top ten list

  19. The top ten list

  20. Analyzing easiness

  21. Analyzing termites

  22. Analyzing termites

  23. Analyzing termites

  24. Analyzing termites

  25. The measure of meaning captures the stem and suffix part x is a stem and y is a suffix

  26. Selectional relation is treated as the predictive power of the stem and suffix easiness easi → easi.er, easi.ly ness → readi.ness, fond.ness, hard.ness eas → eas.ier, eas.ily, eas.ter, eas.ton iness → read.iness,

  27. Selectional relation is treated as the predictive power of the stem and suffix easiness easi → easi.er, easi.ly ness → readi.ness, fond.ness, hard.ness eas → eas.ier, eas.ily, eas.ter, eas.ton iness → read.iness,

  28. Selectional relation is treated as the predictive power of the stem and suffix easiness easi → easi.er, easi.ly ness → readi.ness, fond.ness, hard.ness eas → eas.ier, eas.ily, eas.ter, eas.ton iness → read.iness,

  29. Combining the endings from the stem and the starts from the suffix results in a collection of possible words

  30. The first hypothesis is easi.ness easi → .er, .ly ness → readi., fond., hard. readi.er, readi.ly, fond.er, fond.ly, hard.er, hard.ly 5 positive 1 negative approx 90%

  31. The second hypothesis is eas.iness eas → .ier,.ily,.ter,.ton iness → read. read.ier, read.ily, read.ter, read.ton 1 positive 3 negative, 25%

  32. easi.ness is best on both accounts and is the preferred analysis

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