CSCI 5832 Natural Language Processing Lecture 4 Jim Martin 01 15 19 1 Today 1 24 English Morphology FSAs and Morphology Break FSTs 2 01 15 19 Transition Finite state methods are particularly useful in dealing with a lexicon Lots of devices some with limited memory need access to big lists of words And they need to perform fairly sophisticated tasks with those lists So we ll switch to talking about some facts about words and then come back to computational methods 3 01 15 19 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 4 01 15 19 English Morphology We can also divide morphology up into two broad classes Inflectional Derivational 5 01 15 19 Word Classes By word class we have in mind familiar notions like noun and verb We ll go into the gory details in Chapter 5 Right now we re concerned with word classes because the way that stems and affixes combine is based to a large degree on the word class of the stem 6 01 15 19 Inflectional Morphology Inflectional morphology concerns the combination of stems and affixes where the resulting word Has the same word class as the original Serves a grammatical semantic purpose that is Different from the original But is nevertheless transparently related to the original 7 01 15 19 Nouns and Verbs English Nouns are simple Markers for plural and possessive Verbs are only slightly more complex Markers appropriate to the tense of the verb 8 01 15 19 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 are used to refer to words that follow the rules and those that don t 9 01 15 19 Regular and Irregular Verbs Regulars Walk walks walking walked walked Irregulars Eat eats eating ate eaten Catch catches catching caught caught Cut cuts cutting cut cut 10 01 15 19 Inflectional Morphology So inflectional morphology in English is fairly straightforward But is complicated by the fact that are irregularities 11 01 15 19 Derivational Morphology Derivational morphology is the messy stuff that no one ever taught you Quasi systematicity Irregular meaning change Changes of word class 12 01 15 19 Derivational Examples Converting verbs and adjectives to nouns ation computerize computerization ee appoint appointee er kill killer ness fuzzy fuzziness 13 01 15 19 Derivational Examples Nouns and verbs to adjectives al computation computational able embrace embraceable less clue clueless 14 01 15 19 Compute Many paths are possible Start with compute Computer computerize computerization Computer computerize computerizable But not all paths operations are equally good or even allowable Clue clueable 15 01 15 19 Morpholgy 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 16 01 15 19 Start Simple Regular singular nouns are ok Regular plural nouns have an s on the end Irregulars are ok as is 17 01 15 19 Simple Rules 18 01 15 19 Now Add in the Words 19 01 15 19 Derivational Rules If everything is an accept state how do things ever get rejected 20 01 15 19 Homework How big is your vocabulary 21 01 15 19 Homework Strings are an easy and not very good way to represent texts Normally we want lists of sentences that consist of lists of tokens that ultimately may point to strings representing words lexemes Lists are central to Python and will make your life easy if you let them 22 01 15 19 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 23 01 15 19 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 24 01 15 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 an application spelling correction information retrieval Or simply a link in a chain of processing 25 01 15 19 FSTs 26 01 15 19 Transitions c c a a t t N PL s c c means read a c on one tape and write a c on the other N means read a N symbol on one tape and write nothing on the other PL s means read PL and write an s 27 01 15 19 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 28 01 15 19 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 29 01 15 19 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 30 01 15 19 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 31 01 15 19 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 32 01 15 19 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 33 01 15 19 Generativity Nothing really privileged about the directions We can write from one and read from the other or
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