Morphological Analysis Chapter 3

Morphology Morpheme = "minimal meaning-bearing unit in a language" Morphology handles the formation of words by using morphemes –base form (stem,lemma), e.g., believe –affixes (suffixes, prefixes, infixes), e.g., un-, -able, -ly Morphological parsing = the task of recognizing the morphemes inside a word –e.g., hands, foxes, children Important for many tasks –machine translation, information retrieval, etc. –Parsing, text simplification, etc 2

Morphemes and Words Combine morphemes to create words  Inflection  combination of a word stem with a grammatical morpheme  same word class, e.g. clean (verb), clean-ing (verb)  Derivation  combination of a word stem with a grammatical morpheme  Yields different word class, e.g delight (verb), delight-ful (adj)  Compounding  combination of multiple word stems  Cliticization  combination of a word stem with a clitic  different words from different syntactic categories, e.g. I’ve = I + have 3

Inflectional Morphology word stem + grammatical morphemecat + s only for nouns, verbs, and some adjectives Nouns  plural:  regular: +s, +es irregular: mouse - mice; ox - oxen  many spelling rules: e.g. -y -> -ieslike: butterfly - butterflies  possessive: +'s, +' Verbs  main verbs (sleep, eat, walk)  modal verbs (can, will, should)  primary verbs (be, have, do) 4

Inflectional Morphology (verbs) Verb Inflections for: main verbs (sleep, eat, walk); primary verbs (be, have, do) Morpholog. FormRegularly Inflected Form stemwalkmerge trymap -s formwalksmerges triesmaps -ing participlewalkingmerging tryingmapping past; -ed participlewalkedmerged triedmapped Morph. FormIrregularly Inflected Form stemeatcatch cut -s formeatscatches cuts -ing participleeatingcatching cutting -ed pastatecaught cut -ed participle eatencaught cut 5

Noun Inflections for: regular nouns (cat, hand); irregular nouns(child, ox) Morpholog. FormRegularly Inflected Form stemcathand plural formcatshands Morph. FormIrregularly Inflected Form stemchildox plural formchildrenoxen Inflectional Morphology (nouns) 6

Inflectional and Derivational Morphology (adjectives) Adjective Inflections and Derivations: prefixun-unhappyadjective, negation suffix-lyhappilyadverb, manner suffix -ier, -iest happier, happiest comparatives suffix-nesshappinessnoun plus combinations, like unhappiest, unhappiness. Distinguish different adjective classes, which can or cannot take certain inflectional or derivational forms, e.g. no negation for big. 7

Derivational Morphology (nouns) 8

Derivational Morphology (adjectives) 9

Verb Clitics 10

11 Morpholgy and FSAs We’d like to use the machinery provided by FSAs to capture these facts about morphology  Recognition:  Accept strings that are in the language  Reject strings that are not  In a way that doesn’t require us to in effect list all the words in the language

12 Computational Lexicons Depending on the purpose, computational lexicons have various types of information  Between FrameNet and WordNet, we saw POS, word sense, subcategorization, semantic roles, and lexical semantic relations  For our purposes now, we care about stems, irregular forms, and information about affixes

13 Starting Simply Let’s start simply:  Regular singular nouns listed explicitly in lexicon  Regular plural nouns have an -s on the end  Irregulars listed explicitly too

14 Simple Rules

15 Now Plug in the Words Recognition of valid words But “foxs” isn’t right; we’ll see how to fix that

16 Parsing/Generation vs. Recognition We can now run strings through these machines to recognize strings in the language But recognition is usually not quite what we need  Often if we find some string in the language we might like to assign a structure to it (parsing)  Or we might have some structure and we want to produce a surface form for it (production/generation) Example  From “cats” to “cat +N +PL”

17 Finite State Transducers Add another tape Add extra symbols to the transitions On one tape we read “cats”, on the other we write “cat +N +PL”

18 FSTs

19 Applications The kind of parsing we’re talking about is normally called morphological analysis It can either be An important stand-alone component of many applications (spelling correction, information retrieval) Or simply a link in a chain of further linguistic analysis

20 Transitions 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

21 Ambiguity Recall that in non-deterministic recognition multiple paths through a machine may lead to an accept state. Didn’t matter which path was actually traversed In FSTs the path to an accept state does matter since different paths represent different parses and different outputs will result

22 Ambiguity What’s the right parse (segmentation) for Unionizable Union-ize-able Un-ion-ize-able Each represents a valid path through the morphology machine.

23 Ambiguity There are a number of ways to deal with this problem Simply take the first output found Find all the possible outputs (all paths) and return them all (without choosing) Bias the search so that only one or a few likely paths are explored

24 The Gory Details Of course, its not as easy as “cat +N +PL” “cats” As we saw earlier there are geese, mice and oxen But there are also a whole host of spelling/pronunciation changes that go along with inflectional changes Fox and Foxes vs. Cat and Cats

25 Multi-Tape Machines To deal with these complications, we will add more tapes and use the output of one tape machine as the input to the next So to handle irregular spelling changes we’ll add intermediate tapes with intermediate symbols

26 Multi-Level Tape Machines We use one machine to transduce between the lexical and the intermediate level, and another to handle the spelling changes to the surface tape #

27 Intermediate to Surface The add an “e” rule as in fox^s# --> foxes#

28 Lexical to Intermediate Level

29 Foxes # This arrow should point straight down

30 Notes The transducers may be run in the other direction too (examples in lecture) The transducers are cascaded: The output of one layer serves as the input to the next

31 Overall Scheme # We aren’t covering the overall scheme in any more detail than this