Morphology

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
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
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
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
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
Morphological processes

• Affixes prefix, suffix, infix, circumfix
• Vowel change (umlaut, ablaut)
• Gemination, (partial) reduplication
• Root and pattern
• Stress (or tone) change
• Sandhi

7
Morphophonemics

• Morphemes and allomorphs
• eg plur (e)s, vowel change, y?ies, f?ves, 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
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
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
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
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 f?ves)
• 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
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
Morphological processing

• Stemming
• String-handling approaches
• Regular expressions
• Mapping onto finite-state automata
• 2-level morphology
• Mapping between surface form and lexical
  representation

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
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
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
ExampleJurafsky 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

18
Non-deterministic FSAJurafsky 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

19
An FSA to handle morphology
Spot the deliberate mistake overgeneration
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
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
• spys spies
• Lexicalintermediate foxNPs foxs
• Intermediatesurface foxs foxes

22
Some conventions

• Transitions are marked by
• A non-changing transition xx can be shown
  simply as x
• Wild-cards are shown as _at_
• Empty string shown as e

23
An examplebased on Trost p.42
spys spies
s
p
yi
e
s
e
e
toys toys
t
o
y
0
s
e
e
s
h
e
e
s
e
l
fv
e
w
i
fv
e
s
e
e
24
Using wild cards and loops
s
p
yi
e
s
0
0
t
o
y
0
s
0
0
Can be collapsed into a single FST
25
Another example (JM Fig. 3.9, p.74)
f o x c a t d o g
P s
Ne
q4
q1
g o o s e s h e e p m o u s e
S
Ne
q0
q5
q2
q7
S
g oe oe s e s h e e p m oi uesc e
Ne
P
q6
q3
lexicalintermediate
26
f o x c a t d o g
q1
q0
o
s1
s2
f
x
a
c
t
q0
q1
s3
s4
d
g
o
s5
s6
27

• 0 ff oo xx 1 Ne 4 P ss 7
• 0 ff oo xx 1 Ne 4 S 7
• 0 cc aa tt 1 Ne 4 P ss 7
• 0 ss hh ee pp 2 Ne 5 S 7
• 0 gg oe oe ss ee 3 Ne 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
P s
Ne
q4
q1
g o o s e s h e e p m o u s e
S
Ne
q0
q5
q2
q7
S
g oe oe s e s h e e p m oi uesc e
Ne
P
q6
q3
28
Lexicalsurface mappingJM Fig. 3.14, p.78
f o x N P s f o x s c a t N P s c a t
s
e ? e / x s z __ s
29
0 ff 0 oo 0 xx 1 e 2 ee 3 ss
4 0 0 cc 0 aa 0 tt 0 e 0
ss 0 0
f o x s f o x e s c a t s c a t s
other
q5
e other
z, s, x
s
e
z, s, x
e
ee
s
q0
q1
q4
q2
q3
, other
z, x

30
FST

• But you dont 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
FST compiler http//www.xrce.xerox.com/competencie
s/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 -gt s1, d -gt s2, f -gt s3, g -gt s4. s1 a
-gt s5. s2 o -gt s6. s3 o -gt s7. s4 ltoegt
-gt s8. s5 t -gt s9. s6 g -gt s9. s7 x -gt
s10. s8 ltoegt -gt s11. s9 ltNsgt -gt s12. s10
ltNegt -gt s13. s11 s -gt s14. s12 ltP0gt -gt
fs15. s13 ltPsgt -gt fs15. s14 e -gt s16. fs15
(no arcs) s16 ltN0gt -gt s12.