1. CS 4705 Morphology: Words and their Parts CS 4705 Julia Hirschberg

2. Words In formal languages, words are arbitrary strings In natural languages, words are made up of meaningful subunits called morphemes –Morphemes are abstract concepts denoting entities or relationships –Morphemes may be Stems: the main morpheme of the word Affixes: convey the word’s role, number, gender, etc. cats == cat [stem] + s [suffix] undo == un [prefix] + do [stem]

3. Why do we need to do Morphological Analysis? The study of how words are composed from smaller, meaning-bearing units (morphemes) Applications: –Spelling correction: referece –Hyphenation algorithms: refer-ence –Part-of-speech analysis: googler [N], googling [V] –Text-to-speech: grapheme-to-phoneme conversion hothouse (/T/ or /D/)

4. –Let’s us guess the meaning of unknown words ‘Twas brillig and the slithy toves… Muggles moogled migwiches

5. Morphotactics What are the ‘rules’ for constructing a word in a given language? –Pseudo-intellectual vs. *intellectual-pseudo –Rational-ize vs *ize-rational –Cretin-ous vs. *cretin-ly vs. *cretin-acious Possible ‘rules’ –Suffixes are suffixes and prefixes are prefixes –Certain affixes attach to certain types of stems (nouns, verbs, etc.) –Certain stems can/cannot take certain affixes

6. Semantics: In English, un- cannot attach to adjectives that already have a negative connotation: –Unhappy vs. *unsad –Unhealthy vs. *unsick –Unclean vs. *undirty Phonology: In English, -er cannot attach to words of more than two syllables –great, greater –Happy, happier –Competent, *competenter –Elegant, *eleganter –Unruly, ?unrulier

7. Regular –Walk, walks, walking, walked, walked –Table, tables Irregular –Eat, eats, eating, ate, eaten –Catch, catches, catching, caught, caught –Cut, cuts, cutting, cut, cut –Goose, geese Regular and Irregular Morphology

8. Morphological Parsing Algorithms to use these regularities and known irregularities to parse words into their morphemes Cats cat +N +PL Cat cat +N +SG Cities city +N +PL Merging merge +V +Present-participle Caught catch +V +past-participle

9. Morphology and Finite State Automata We can use the machinery provided by FSAs to capture facts about morphology Accept strings that are in the language Reject strings that are not Do this in a way that doesn’t require us to in effect list all the words in the language

10. How do we build a Morphological Analyzer? Lexicon: list of stems and affixes (w/ corresponding part of speech (p.o.s.)) Morphotactics of the language: model of how and which morphemes can be affixed to a stem Orthographic rules: spelling modifications that may occur when affixation occurs –in  il in context of l (in- + legal) Most morphological phenomena can be described with regular expressions – so finite state techniques often used to represent morphological processes

11. A Simple Example Regular singular nouns are ok Regular plural nouns have an -s on the end Irregulars are ok as is

12. Simple English NP FSA

13. Expand the Arcs with Stems and Affixes cat dog child

14. We can now run strings through these machines to recognize strings in the language Accept words that are ok Reject words that are not But is this enough? We often want to know the structure of a word (parsing) Or we may have a stem and want to produce a surface form (production/generation) Example From “cats” to “cat +N +PL” From “cat + N + PL” to “cats”

15. Finite State Transducers (FSTs) The simple story Add another tape Add extra symbols to the transitions On one tape we read “cats” -- on the other we write “cat +N +PL” Or vice versa…

16. Kimmo Koskenniemi’s two-level morphology Idea: a word is a relationship between lexical level (its morphemes) and surface level (its orthography) Koskenniemi 2-level Morphology

17. c:c means read a c on one tape and write a c on the other +N:ε means read a +N symbol on one tape and write nothing on the other +PL:s means read +PL and write an s c:ca:at:t +N:ε + PL:s

18. Not So Simple Of course, its not as easy as “cat +N +PL” “cats” What do we do about geese, mice, oxen? Many spelling/pronunciation changes that go along with inflectional changes, e.g. Fox and Foxes

19. Multi-Tape Machines Solution for complex changes: –Add more tapes –Use output of one tape machine as input to the next So to handle irregular spelling changes, add intermediate tapes with intermediate symbols

20. Example of a Multi-Tape Machine We use one machine to transduce between the lexical and the intermediate level, and another to transduce between the intermediate and the surface tapes

21. FST Fragment: Lexical to Intermediate

22. FST Fragment: Intermediate to Surface The add an “e” rule as in fox^s# foxes#

23. Putting Them Together

24. Practical Uses The kind of parsing we’re talking about is normally called morphological analysis It can be An important stand-alone component of an application (spelling correction, information retrieval, part-of-speech tagging,…) Or simply a link in a chain of processing (machine translation, parsing,…)

25. Porter Stemmer Standard, very popular and usable stemmer (IR, IE) Sequence of cascaded rewrite rules, e.g. –IZE  ε (e.g. unionize  union) –CY  T (e.g. frequency  frequent) –ING  ε, if stem contains vowel (motoring  motor) Can be implemented as a lexicon-free FST (many implementations available on the web)

26. Note: Morphology Differs by Language Languages differ in how they encode morphological information –Isolating languages (e.g. Cantonese) have no affixes: each word usually has 1 morpheme –Agglutinative languages (e.g. Finnish, Turkish) are composed of prefixes and suffixes added to a stem (like beads on a string) – each feature realized by a single affix, e.g. Finnish epäjärjestelmällistyttämättömyydellänsäkäänköhän ‘Wonder if he can also... with his capability of not causing things to be unsystematic’

27. –Polysynthetic languages (e.g. Inuit languages) express much of their syntax in their morphology, incorporating a verb’s arguments into the verb, e.g. Western Greenlandic Aliikusersuillammassuaanerartassagaluarpaalli. aliiku-sersu-i-llammas-sua-a-nerar-ta-ssa-galuar-paal-li entertainment-provide-SEMITRANS-one.good.at-COP- say.that-REP-FUT-sure.but-3.PL.SUBJ/3SG.OBJ-but 'However, they will say that he is a great entertainer, but...'SEMITRANSCOPFUTOBJ –So….different languages may require very different morphological analyzers

28. Summing Up Regular expressions and FSAs can represent subsets of natural language as well as regular languages –Both representations may be difficult for humans to understand for any real subset of a language –Can be hard to scale up: e.g., when many choices at any point (e.g. surnames) –But quick, powerful and easy to use for small problems –AT&T Finite State Toolkit does scale Next class: –Read Ch 4

29. Morphological Representations: Evidence from Human Performance Hypotheses: –Full listing hypothesis: words listed –Minimum redundancy hypothesis: morphemes listed Experimental evidence: –Priming experiments (Does seeing/hearing one word facilitate recognition of another?) suggest neither –Regularly inflected forms (e.g. cars) prime stem (car) but not derived forms (e.g. management, manage)

30. –But spoken derived words can prime stems if they are semantically close (e.g. government/govern but not department/depart) Speech errors suggest affixes must be represented separately in the mental lexicon –‘easy enoughly’ for ‘easily enough’

31. Concatenative vs. Non-concatenative Morphology Semitic root-and-pattern morphology –Root (2-4 consonants) conveys basic semantics (e.g. Arabic /ktb/) –Vowel pattern conveys voice and aspect –Derivational template (binyan) identifies word class

32. TemplateVowel Pattern activepassive CVCVCkatabkutibwrite CVCCVCkattabkuttibcause to write CVVCVCka:tabku:tibcorrespond tVCVVCVCtaka:tabtuku:tib write each other nCVVCVCnka:tabnku:tibsubscribe CtVCVCktatabktutibwrite stVCCVCstaktabstuktibdictate