Morphology: Wordsand their Parts

CS 4705

Slides adapted from Jurafsky, Martin Hirschberg and Dorr.

English Morphology

Morphology is the study of the ways that words are built up from smaller meaningful units called morphemes

We can usefully divide morphemes into two classes– Stems: The core meaning bearing units– Affixes: Bits and pieces that adhere to stems to

change their meanings and grammatical functions

Nouns and Verbs (English)

Nouns are simple (not really)– Markers for plural and possessive

Verbs are only slightly more complex– Markers appropriate to the tense of the verb

Regulars and Irregulars

Ok so it gets a little complicated by the fact that some words misbehave (refuse to follow the rules)– Mouse/mice, goose/geese, ox/oxen– Go/went, fly/flew

The terms regular and irregular will be used to refer to words that follow the rules and those that don’t.

Regular and Irregular Nouns and Verbs

Regulars…– Walk, walks, walking, walked, walked– Table, tables

Irregulars– Eat, eats, eating, ate, eaten– Catch, catches, catching, caught, caught– Cut, cuts, cutting, cut, cut– Goose, geese

Why care about morphology?

Spelling correction: referece– Morphology in machine translation

Spanish words quiero and quieres are both related to querer ‘want’

– Hyphenation algorithms: refer-ence– Part-of-speech analysis: google, googler– Text-to-speech: grapheme-to-phoneme conversion

hothouse (/T/ or /D/)

– Allows us to guess at meaning ‘Twas brillig and the slithy toves… Muggles moogled migwiches

Concatenative Morphology

Morpheme+Morpheme+Morpheme+… Stems: often called lemma, base form, root, lexeme

– hope+ing hoping hop hopping

Affixes– Prefixes: Antidisestablishmentarianism– Suffixes: Antidisestablishmentarianism– Infixes: hingi (borrow) – humingi (borrower) in Tagalog– Circumfixes: sagen (say) – gesagt (said) in German

What useful information does morphology give us?

Different things in different languages– Spanish: hablo, hablaré/ English: I speak, I will speak– English: book, books/ Japanese: hon, hon

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’ – Inflectional languages (e.g. English) merge different features

into a single affix (e.g. ‘s’ in likes indicates both person and tense); and the same feature can be realized by different affixes

– 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-lientertainment-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 ...'

– So….different languages may require very different morphological analyzers

What we want

Something to automatically do the following kinds of mappings:

Cats cat +N +PL Cat cat +N +SG Citiescity +N +PL Merging merge +V +Present-participle

Caught catch +V +past-participle

Morphology Can Help Define Word Classes

AKA morphological classes, parts-of-speech Closed vs. open (function vs. content) class

words– Pronoun, preposition, conjunction, determiner,…– Noun, verb, adverb, adjective,…

Identifying word classes is useful for almost any task in NLP, from translation to speech recognition to topic detection…very basic semantics

(English) Inflectional Morphology

Word stem + grammatical morpheme different forms of same word– Usually produces word of same class– Usually serves a syntactic or grammatical function

(e.g. agreement)like likes or likedbird birds

Nominal morphology– Plural forms

s or es Irregular forms (goose/geese)

Mass vs. count nouns (fish/fish(es), email or emails?)

– Possessives (cat’s, cats’)

Verbal inflection– Main verbs (sleep, like, fear) relatively regular

-s, ing, ed And productive: emailed, instant-messaged, faxed, homered But some are not:

– eat/ate/eaten, catch/caught/caught

– Primary (be, have, do) and modal verbs (can, will, must) often irregular and not productive

Be: am/is/are/were/was/been/being

– Irregular verbs few (~250) but frequently occurring

Derivational Morphology

Word stem + syntactic/grammatical morpheme new words– Usually produces word of different class– Incomplete process: derivational morphs cannot

be applied to just any member of a class

Verbs --> nouns– -ize verbs -ation nouns– generalize, realize generalization, realization– synthesize but not synthesization

Verbs, nouns adjectives– embrace, pity embraceable, pitiable– care, wit careless, witless

Adjective adverb– happy happily

Process selective in unpredictable ways– Less productive: nerveless/*evidence-less,

malleable/*sleep-able, rar-ity/*rareness– Meanings of derived terms harder to predict by

rule– clueless, careless, nerveless, sleepless

Compounding

Two base forms join to form a new word– Bedtime, Weinerschnitzel, Rotwein– Careful? Compound or derivation?

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

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

Morphological Parsing

These regularities enable us to create software to parse words into their component parts

Morphology and FSAs

We’d like to use the machinery provided by FSAs to capture facts about morphology• Ie. Accept strings that are in the language• And reject strings that are not• And do it in a way that doesn’t require us to in

effect list all the words in the language

What do we need to build a morphological parser?

Lexicon: list of stems and affixes (w/ corresponding 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

Start Simple

Regular singular nouns are ok Regular plural nouns have an -s on the end Irregulars are ok as is

Simple Rules

Now Add in the Words

Derivational morphology: adjective fragment

q3

q5

q4

q0

q1 q2un-

adj-root1

-er, -ly, -est

adj-root1

adj-root2

-er, -est

• Adj-root1: clear, happi, real (clearly)

• Adj-root2: big, red (*bigly)

Parsing/Generation vs. Recognition

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 recognition is usually not quite what we need • Often if we find some string in the language we might like to find the

structure in it (parsing)• Or we have some structure and we want to produce a surface form

(production/generation) Example

• From “cats” to “cat +N +PL”

Finite State Transducers

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”

Applications

The kind of parsing we’re talking about is normally called morphological analysis

It can either be • An important stand-alone component of an

application (spelling correction, information retrieval)

• Or simply a link in a chain of processing

FSTs

Kimmo Koskenniemi’s two-level morphologyIdea: word is a relationship between lexical level (its morphemes) and surface level (its orthography)

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:c a:a t:t +N:ε +PL:s

Typical Uses

Typically, we’ll read from one tape using the first symbol on the machine transitions (just as in a simple FSA).

And we’ll write to the second tape using the other symbols on the transitions.

In general, FSTs can be used for– Translators (Hello:Ciao)– Parser/generators (Hello:How may I help you?)– As well as Kimmo-style morphological parsing

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 differ paths represent different parses and different outputs will result

Ambiguity

What’s the right parse (segmentation) for• Unionizable• Union-ize-able• Un-ion-ize-able

Each represents a valid path through the derivational morphology machine.

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

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

• Cats vs Dogs• Fox and Foxes

Multi-Tape Machines

To deal with this we can simply 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

Generativity

Nothing really privileged about the directions. We can write from one and read from the

other or vice-versa. One way is generation, the other way is

analysis

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

Lexical to Intermediate Level

Intermediate to Surface

The add an “e” rule as in fox^s# <-> foxes#

Foxes

Note

A key feature of this machine is that it doesn’t do anything to inputs to which it doesn’t apply.

Meaning that they are written out unchanged to the output tape.

Overall Scheme

We now have one FST that has explicit information about the lexicon (actual words, their spelling, facts about word classes and regularity).• Lexical level to intermediate forms

We have a larger set of machines that capture orthographic/spelling rules.• Intermediate forms to surface forms

Overall Scheme

Cascades

This is a scheme that we’ll see again and again.• Overall processing is divided up into distinct

rewrite steps• The output of one layer serves as the input to the

next• The intermediate tapes may or may not wind up

being useful in their own right

Porter Stemmer (1980)

Used for tasks in which you only care about the stem– IR, modeling given/new distinction, topic detection, document similarity

Lexicon-free morphological analysis Cascades rewrite rules (e.g. misunderstanding -->

misunderstand --> understand --> …) Easily implemented as an FST with rules e.g.

– ATIONAL ATE

– ING ε Not perfect ….

– Doing doe

Policy police

Does stemming help?– IR, little– Topic detection, more

Summing Up

FSTs provide a useful tool for implementing a standard model of morphological analysis, Kimmo’s two-level morphology

But for many tasks (e.g. IR) much simpler approaches are still widely used, e.g. the rule-based Porter Stemmer

Next time: – Read Ch 4

HW1 assigned; see web page: http://www.cs.columbia.edu/~kathy/NLP