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
There at least three conditions on structuring a word w into x. y
There at least three conditions on structuring w into x. y x is a stem and y is a suffix
There at least three conditions on structuring w into x. y x is a stem and y is a suffix y selects x
There 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
Arguments for x being a stem carries over to an argument that y is a suffix
If x is a stem then x has meaning
If x is a stem then x has meaning stem(x) → meaning(x)
If x is a stem then x has meaning stem(x) → meaning(x) word(x) → meaning(x)
Being a stem is translated into being a word
Being a stem is translated into being a word Pr( stem(x)| w=x. y) ~ Pr(word(x)| w=x. y)
Being a stem is translated into being a word Pr( stem(x)| w=x. y) ~ Pr(word(x)| w=x. y)
Being a stem is translated into being a word Pr( stem(x)| w=x. y) ~ Pr(word(x)| w=x. y)
A beta distribution is used for assigning a probability based on the proportion
A beta distribution is used for assigning a probability based on the proportion beta(positive, negative)
The top ten list Morph less ' 's Ratio 95 93 92 SD 1 1 0 Prob 94 92 92 Pos 368 1723 9783 Neg 18 133 857 ship like house 'll head fish stone 91 91 88 88 87 2 2 3 3 4 4 4 89 89 88 88 85 84 83 167 140 75 105 61 66 66 16 14 7 11 8 9 10
The top ten list Morph less ' 's Ratio 95 93 92 SD 1 1 0 Prob 94 92 92 Pos 368 1723 9783 Neg 18 133 857 ship like house 'll head fish stone 91 91 88 88 87 2 2 3 3 4 4 4 89 89 88 88 85 84 83 167 140 75 105 61 66 66 16 14 7 11 8 9 10
The top ten list Morph less ' 's Ratio 95 93 92 SD 1 1 0 Prob 94 92 92 Pos 368 1723 9783 Neg 18 133 857 ship like house 'll head fish stone 91 91 88 88 87 2 2 3 3 4 4 4 89 89 88 88 85 84 83 167 140 75 105 61 66 66 16 14 7 11 8 9 10
Analyzing easiness easines easine ness iness ss 78 46 42 6 4
Analyzing termites Suffix Ratio SD Prob Pos Neg s 42 0 42 18098 25001 ites 43 4 40 78 102 es 23 0 23 2094 6925 tes 19 1 17 211 927
Analyzing termites Suffix Ratio SD Prob Pos Neg s 42 0 42 18098 25001 ites 43 4 40 78 102 es 23 0 23 2094 6925 tes 19 1 17 211 927
Analyzing termites Suffix Ratio SD Prob Pos Neg s 42 0 42 18098 25001 ites 43 4 40 78 102 es 23 0 23 2094 6925 tes 19 1 17 211 927
Analyzing termites Suffix Ratio SD Prob Pos Neg s 42 0 42 18098 25001 ites 43 4 40 78 102 es 23 0 23 2094 6925 tes 19 1 17 211 927
The measure of meaning captures the stem and suffix part x is a stem and y is a suffix
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,
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,
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,
Combining the endings from the stem and the starts from the suffix results in a collection of possible words
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%
The second hypothesis is eas. iness eas →. ier, . ily, . ter, . ton iness → read. ier, read. ily, read. ter, read. ton 1 positive 3 negative, 25%
easi. ness is best on both accounts and is the preferred analysis
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