CSA3050: Natural Language Algorithms

1. CSA3050: Natural Language Algorithms Morphological Parsing CSA3050 NLP Algorithms

2. Morphology • Morphemes: The smallest unit in a word that bear some meaning, such as rabbit and s, are called morphemes. • Combination of morphemes to form words that are legal in some language. • Two kinds of morphology • Inflectional • Derivational CSA3050 NLP Algorithms

3. Inflectional+s plural+ed past category preserving productive: always applies (esp. new words, e.g. fax) systematic: same semantic effect Derivational+ment category changingescape+ment not completely productive: detractment* not completely systematic: apartment Inflectional/DerivationalMorphology CSA3050 NLP Algorithms

4. Noun Inflections CSA3050 NLP Algorithms

5. Morphological Parsing Output Analysis cat N PL Input Word cats Morphological Parser • Output is a string of morphemes • Reversibility? CSA3050 NLP Algorithms

6. Morphological Parsing • The goal of morphological parsing is to find out what morphemes a given word is built from. mouse mouse N SG mice mouse N PL foxes fox N PL CSA3050 NLP Algorithms

7. 2 Steps • Split word up into its possible components, using + to indicate possible morpheme boundaries. cats cat + s foxes fox + s foxes foxe + s • Look up the categories of the stems and the meaning of the affixes, using a lexicon of stems and affixescat + s cat + NP + PL fox + s fox + N + PL. CSA3050 NLP Algorithms

8. Step 1: Surface IntermediateFST CSA3050 NLP Algorithms

9. Step 1: Surface IntermediateOperation CSA3050 NLP Algorithms

10. 2. Intermediate Morphemes Possible inputs to the transducer are: • Regular noun stem: cat • Regular noun stem + s: cat+s • Singular irregular noun stem: mouse • Plural irregular noun stem: mice CSA3050 NLP Algorithms

11. 2. Intermediate MorphemesTransducer CSA3050 NLP Algorithms

12. Handling Stems cat /cat mice/mouse CSA3050 NLP Algorithms

13. Completed Stage 2 CSA3050 NLP Algorithms

14. Joining Stages 1 and 2 • If the two transducers run in a cascade (i.e. we let the second transducer run on the output of the first one), we can do a morphological parse of (some) English noun phrases. • We can change also the direction of translation (in translation mode). • This transducer can also be used for generating a surface form from an underlying form. CSA3050 NLP Algorithms

15. Prolog • The transducer specifications we have seen translate easily into Prolog format except for the other transition. • arc(1,3,z:z).arc(1,3,s:s).arc(1,3,x:x).arc(1,2,#:+).arc(1,3,<other>). CSA3050 NLP Algorithms

16. Handling other arcs arc(1,3,z:z) :- !.arc(1,3,s:s) :- !.arc(1,3,x:x) :- !.arc(1,2,#:+) :- !.arc(1,3,X:X) :- !. CSA3050 NLP Algorithms

17. Combining Rules • Consider the word “berries”. • Two rules are involved • berry + s • y → ie under certain circumstances. • Combinations of such rules can be handled in two ways • Cascade, i.e. sequentially • Parallel • Algorithms exist for combining transducers together in series or in parallel. • Such algorithms involve computations over regular relations. CSA3050 NLP Algorithms

18. FSA 3 Related Frameworks REGULAR LANGUAGES REGULAR EXPRESSIONS CSA3050 NLP Algorithms

19. FINITE STATE TRANSDUCERS REGULAR RELATIONS REGULAR RELATIONS AUGMENTED REGULAR EXPRESSIONS CSA3050 NLP Algorithms

20. Putting it all together execution of FSTi takes place in parallel CSA3050 NLP Algorithms

21. Kaplan and KayThe Xerox View FSTi are aligned but separate FSTi intersected together CSA3050 NLP Algorithms

22. Summary • Morphological processing can be handled by finite state machinery • Finite State Transducers are formally very similar to Finite State Automata. • They are formally equivalent to regular relations, i.e. sets of pairings of sentences of regular languages. CSA3050 NLP Algorithms