![Morphology
See
Harald Trost “Morphology”. Chapter 2 of R Mitkov (ed.) The
Oxford Handbook of Computational Linguistics, Oxford
(2004): OUP
D Jurafsky & JH Martin: Speech and Language Processing,
Upper Saddle River NJ (2000): Prentice Hall, Chapter 3
[quite technical]](https://image.slidesharecdn.com/morphology-220922141957-c059a204/85/Morphology-ppt-1-320.jpg)
![27
q0
q6
q5
q4
q3
q2
q1
q7
g o o s e
s h e e p
m o u s e
g o:e o:e s e
s h e e p
m o:i u:εs:c e
N:ε
N:ε
N:ε
P:^ s #
S:#
S:#
P:#
[0] f:f o:o x:x [1] N:ε [4] P:^ s:s #:# [7]
[0] f:f o:o x:x [1] N:ε [4] S:# [7]
[0] c:c a:a t:t [1] N:ε [4] P:^ s:s #:# [7]
[0] s:s h:h e:e p:p [2] N:ε [5] S:# [7]
[0] g:g o:e o:e s:s e:e [3] N:ε [5] P:# [7]
f o x N P s # : f o x ^ s #
f o x N S : f o x #
c a t N P s # : c a t ^ s #
s h e e p N S : s h e e p #
g o o s e N P : g e e s e #
f o x
c a t
d o g](https://image.slidesharecdn.com/morphology-220922141957-c059a204/85/Morphology-ppt-27-320.jpg)
![29
f o x ^ s # f o x e s #
c a t ^ s # : c a t ^ s #
q5
q4
q0 q2 q3
q1
^: ε
#
other
other
z, s, x
z, s, x
#, other z, x
^:ε
s ^:ε
ε:e s
#
[0] f:f [0] o:o [0] x:x [1] ^:ε [2] ε:e [3] s:s [4] #:# [0]
[0] c:c [0] a:a [0] t:t [0] ^:ε [0] s:s [0] #:# [0]](https://image.slidesharecdn.com/morphology-220922141957-c059a204/85/Morphology-ppt-29-320.jpg)
![31
FST compiler
http://www.xrce.xerox.com/competencies/content-analysis/fsCompiler/fsinput.html
[d o g N P .x. d o g s ] |
[c a t N P .x. c a t s ] |
[f o x N P .x. f o x e s ] |
[g o o s e N P .x. g e e s e]
s0: c -> s1, d -> s2, f -> s3, g -> s4.
s1: a -> s5.
s2: o -> s6.
s3: o -> s7.
s4: <o:e> -> s8.
s5: t -> s9.
s6: g -> s9.
s7: x -> s10.
s8: <o:e> -> s11.
s9: <N:s> -> s12.
s10: <N:e> -> s13.
s11: s -> s14.
s12: <P:0> -> fs15.
s13: <P:s> -> fs15.
s14: e -> s16.
fs15: (no arcs)
s16: <N:0> -> s12.
s0
s3
s2
s1
s4
c
d
f
g](https://image.slidesharecdn.com/morphology-220922141957-c059a204/85/Morphology-ppt-31-320.jpg)
Morphology.ppt
- 1. Morphology See Harald Trost “Morphology”. Chapter 2 of R Mitkov (ed.) The Oxford Handbook of Computational Linguistics, Oxford (2004): OUP D Jurafsky & JH Martin: Speech and Language Processing, Upper Saddle River NJ (2000): Prentice Hall, Chapter 3 [quite technical]
- 2. 2 Morphology - reminder • Internal analysis of word forms • morpheme – allomorphic variation • Words usually consist of a root plus affix(es), though some words can have multiple roots, and some can be single morphemes • lexeme – abstract notion of group of word forms that ‘belong’ together – lexeme ~ root ~ stem ~ base form ~ dictionary (citation) form
- 3. 3 Role of morphology • Commonly made distinction: inflectional vs derivational • Inflectional morphology is grammatical – number, tense, case, gender • Derivational morphology concerns word building – part-of-speech derivation – words with related meaning
- 4. 4 Inflectional morphology • Grammatical in nature • Does not carry meaning, other than grammatical meaning • Highly systematic, though there may be irregularities and exceptions – Simplifies lexicon, only exceptions need to be listed – Unknown words may be guessable • Language-specific and sometimes idiosyncratic • (Mostly) helpful in parsing
- 5. 5 Derivational morphology • Lexical in nature • Can carry meaning • Fairly systematic, and predictable up to a point – Simplifies description of lexicon: regularly derived words need not be listed – Unknown words may be guessable • But … – Apparent derivations have specialised meaning – Some derivations missing • Languages often have parallel derivations which may be translatable
- 6. 6 Morphological processes • Affixes: prefix, suffix, infix, circumfix • Vowel change (umlaut, ablaut) • Gemination, (partial) reduplication • Root and pattern • Stress (or tone) change • Sandhi
- 7. 7 Morphophonemics • Morphemes and allomorphs – eg {plur}: +(e)s, vowel change, yies, fves, um a, , ... • Morphophonemic variation – Affixes and stems may have variants which are conditioned by context • eg +ing in lifting, swimming, boxing, raining, hoping, hopping – Rules may be generalisable across morphemes • eg +(e)s in cats, boxes, tomatoes, matches, dishes, buses • Applies to both {plur} (nouns) and {3rd sing pres} (verbs)
- 8. 8 Morphology in NLP • Analysis vs synthesis – what does dogs mean? vs what is the plural of dog? • Analysis – Need to identify lexeme • Tokenization • To access lexical information – Inflections (etc) carry information that will be needed by other processes (eg agreement useful in parsing, inflections can carry meaning (eg tense, number) – Morphology can be ambiguous • May need other process to disambiguate (eg German –en) • Synthesis – Need to generate appropriate inflections from underlying representation
- 9. 9 Morphology in NLP • String-handling programs can be written • More general approach – formalism to write rules which express correspondence between surface and underlying form (eg dogs = dog +{plur}) – Computational algorithm (program) which can apply those rules to actual instances – Especially of interest if rules (though not program) is independent of direction: analysis or synthesis
- 10. 10 Role of lexicon in morphology • Rules interact with the lexicon – Obviously category information • eg rules that apply to nouns – Note also morphology-related subcategories • eg “er” verbs in French, rules for gender agreement – Other lexical information can impact on morphology • eg all fish have two forms of the plural (+s and ) • in Slavic languages case inflections differ for inanimate and animate nouns)
- 11. 11 Problems with rules • Exceptions have to be covered – Including systematic irregularities – May be a trade-off between treating something as a small group of irregularities or as a list of unrelated exceptions (eg French irregular verbs, English fves) • Rules must not over/under-generate – Must cover all and only the correct cases – May depend on what order the rules are applied in
- 12. 12 Tokenization • The simplest form of analysis is to reduce different word forms into tokens • Also called “normalization” • For example, if you want to count how many times a given ‘word’ occurs in a text • Or you want to search for texts containing certain ‘words’ (e.g. Google)
- 13. 13 Morphological processing • Stemming • String-handling approaches – Regular expressions – Mapping onto finite-state automata • 2-level morphology – Mapping between surface form and lexical representation
- 14. 14 Stemming • Stemming is the particular case of tokenization which reduces inflected forms to a single base form or stem • (Recall our discussion of stem ~ base form ~ dictionary form ~ citation form) • Stemming algorithms are basic string- handling algorithms, which depend on rules which identify affixes that can be stripped
- 15. 15 Finite state automata • A finite state automaton is a simple and intuitive formalism with straightforward computational properties (so easy to implement) • A bit like a flow chart, but can be used for both recognition (analysis) and generation • FSAs have a close relationship with “regular expressions”, a formalism for expressing strings, mainly used for searching texts, or stipulating patterns of strings
- 16. 16 Finite state automata • A bit like a flow chart, but can be used for both recognition and generation • “Transition network” • Unique start point • Series of states linked by transitions • Transitions represent input to be accounted for, or output to be generated • Legal exit-point(s) explicitly identified
- 17. 17 Example Jurafsky & Martin, Figure 2.10 • Loop on q3 means that it can account for infinite length strings • “Deterministic” because in any state, its behaviour is fully predictable q0 q1 q2 q3 q4 b a a ! a
- 18. 18 Non-deterministic FSA Jurafsky & Martin, Figure 2.18 • At state q2 with input “a” there is a choice of transitions • We can also have “jump” arcs (or empty transitions), which also introduce non- determinism q0 q1 q2 q3 q4 b a a ! a 2.19 ε
- 19. 19 An FSA to handle morphology q0 q1 q2 q6 q3 f x o e c q5 q4 s r q7 y i Spot the deliberate mistake: overgeneration
- 20. 20 Finite State Transducers • A “transducer” defines a relationship (a mapping) between two things • Typically used for “two-level morphology”, but can be used for other things • Like an FSA, but each state transition stipulates a pair of symbols, and thus a mapping
- 21. 21 Finite State Transducers • Three functions: – Recognizer (verification): takes a pair of strings and verifies if the FST is able to map them onto each other – Generator (synthesis): can generate a legal pair of strings – Translator (transduction): given one string, can generate the corresponding string • Mapping usually between levels of representation – spy+s : spies – Lexical:intermediate foxNPs : fox^s – Intermediate:surface fox^s : foxes
- 22. 22 Some conventions • Transitions are marked by “:” • A non-changing transition “x:x” can be shown simply as “x” • Wild-cards are shown as “@” • Empty string shown as “ε”
- 23. 23 An example based on Trost p.42 s p y:i +:e s #:ε #:ε t o y +:0 s #:ε #:ε s h e +:e s #:ε #:ε l f:v w i f:v e s #:ε #:ε #spy+s# : spies #toy+s# : toys
- 24. 24 Using wild cards and loops s p y:i +:e s #:0 #:0 t o y +:0 s #:0 #:0 @ #:0 y:i +:e y +:0 s #:0 Can be collapsed into a single FST:
- 25. 25 Another example (J&M Fig. 3.9, p.74) q0 q6 q5 q4 q3 q2 q1 q7 f o x c a t d o g g o o s e s h e e p m o u s e g o:e o:e s e s h e e p m o:i u:εs:c e N:ε N:ε N:ε P:^ s # S:# S:# P:# lexical:intermediate
- 26. 26 q0 q1 f o x c a t d o g q0 q1 f s1 s2 s3 s4 s5 s6 c d o a o x t g
- 27. 27 q0 q6 q5 q4 q3 q2 q1 q7 g o o s e s h e e p m o u s e g o:e o:e s e s h e e p m o:i u:εs:c e N:ε N:ε N:ε P:^ s # S:# S:# P:# [0] f:f o:o x:x [1] N:ε [4] P:^ s:s #:# [7] [0] f:f o:o x:x [1] N:ε [4] S:# [7] [0] c:c a:a t:t [1] N:ε [4] P:^ s:s #:# [7] [0] s:s h:h e:e p:p [2] N:ε [5] S:# [7] [0] g:g o:e o:e s:s e:e [3] N:ε [5] P:# [7] f o x N P s # : f o x ^ s # f o x N S : f o x # c a t N P s # : c a t ^ s # s h e e p N S : s h e e p # g o o s e N P : g e e s e # f o x c a t d o g
- 28. 28 Lexical:surface mapping J&M Fig. 3.14, p.78 ε e / {x s z} ^ __ s # f o x N P s # : f o x ^ s # c a t N P s # : c a t ^ s # q5 q4 q0 q2 q3 q1 ^: ε # other other z, s, x z, s, x #, other z, x ^:ε s ^:ε ε:e s #
- 29. 29 f o x ^ s # f o x e s # c a t ^ s # : c a t ^ s # q5 q4 q0 q2 q3 q1 ^: ε # other other z, s, x z, s, x #, other z, x ^:ε s ^:ε ε:e s # [0] f:f [0] o:o [0] x:x [1] ^:ε [2] ε:e [3] s:s [4] #:# [0] [0] c:c [0] a:a [0] t:t [0] ^:ε [0] s:s [0] #:# [0]
- 30. 30 FST • But you don’t have to draw all these FSTs • They map neatly onto rule formalisms • What is more, these can be generated automatically • Therefore, slightly different formalism
- 31. 31 FST compiler http://www.xrce.xerox.com/competencies/content-analysis/fsCompiler/fsinput.html [d o g N P .x. d o g s ] | [c a t N P .x. c a t s ] | [f o x N P .x. f o x e s ] | [g o o s e N P .x. g e e s e] s0: c -> s1, d -> s2, f -> s3, g -> s4. s1: a -> s5. s2: o -> s6. s3: o -> s7. s4: <o:e> -> s8. s5: t -> s9. s6: g -> s9. s7: x -> s10. s8: <o:e> -> s11. s9: <N:s> -> s12. s10: <N:e> -> s13. s11: s -> s14. s12: <P:0> -> fs15. s13: <P:s> -> fs15. s14: e -> s16. fs15: (no arcs) s16: <N:0> -> s12. s0 s3 s2 s1 s4 c d f g