1 / 19

Morphological Parsing

Morphological Parsing. CS 4705. Parsing. Taking a surface input and analyzing its components and underlying structure Morphological parsing : taking a word or string of words as input and identifying the stems and affixes (and possibly interpreting these) E.g .:

rowa
Download Presentation

Morphological Parsing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Morphological Parsing CS 4705 CS 4705

  2. Parsing • Taking a surface input and analyzing its components and underlying structure • Morphological parsing: taking a word or string of words as input and identifying the stems and affixes (and possibly interpreting these) • E.g.: • goose goose +N +SG or goose + V • geese  goose +N +PL • gooses  goose +V +3SG • Bracketing: indecipherable [in [ [de [cipher] ] able] ]

  3. Why ‘parse’ words? • To find stems • Simple key to word similarity • Yellow, yellowish, yellows, yellowed, yellowing… • To find affixes and the information they convey • ‘ed’ signals a verb • ‘ish’ an adjective • ‘s’? • Morphological parsing provides information about a word’s semantics and the syntactic role it plays in a sentence

  4. Some Practical Applications • For spell-checking • Is muncheble a legal word? • To identify a word’s part-of-speech(pos) • For sentence parsing, for machine translation, … • To identify a word’s stem • For information retrieval • Why not just list all word forms in a lexicon?

  5. 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

  6. Using FSAs to Represent English Plural Nouns • English nominal inflection plural (-s) reg-n q0 q1 q2 irreg-pl-n irreg-sg-n • Inputs: cats, geese, goose

  7. q1 q2 q0 adj-root1 • Derivational morphology: adjective fragment -er, -ly, -est un- adj-root1 q5 q3 q4  -er, -est adj-root2 • Adj-root1: clear, happi, real (clearly) • Adj-root2: big, red (*bigly)

  8. FSAs can also represent the Lexicon • Expand each non-terminal arc in the previous FSA into a sub-lexicon FSA (e.g. adj_root2 = {big, red}) and then expand each of these stems into its letters (e.g. red  r e d) to get a recognizer for adjectives e r q1 q2 ε q3 q0 b d q4 q5 i g q6

  9. But….. • Covering the whole lexicon this way will require very large FSAs with consequent search and maintenance problems • Adding new items to the lexicon means recomputing the whole FSA • Non-determinism • Some stems require modification when they acquire affixes • FSAs tell us whether a word is in the language or not – but usually we want to know more: • What is the stem? • What are the affixes and what sort are they? • We used this information to recognize the word: why can’t we store it?

  10. cats cat +N +PL (a plural NP) Kimmo Koskenniemi’s two-level morphology Idea: word is a relationship betweenlexical level (its morphemes) and surface level (its orthography) Morphological parsing : find the mapping (transduction) between lexical and surface levels Parsing with Finite State Transducers lexical surface

  11. Finite State Transducers can represent this mapping • FSTs map between one set of symbols and another using a FSA whose alphabet  is composed of pairs of symbols from input and output alphabets • 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

  12. FST is a 5-tuple consisting of • Q: set of states {q0,q1,q2,q3,q4} • : an alphabet of complex symbols, each an i/o pair s.t. i  I (an input alphabet) and o  O (an output alphabet) and  is in I x O • q0: a start state • F: a set of final states in Q {q4} • (q,i:o): a transition function mapping Q x  to Q • Quizzical Cow  Emphatic Sheep o:a m:b o:a o:a ?:! q0 q1 q2 q3 q4

  13. FST for a 2-level Lexicon • E.g. c:c a:a t:t q3 q0 q1 q2 g e q4 q5 q6 q7 o:e o:e s

  14. c a t +N +PL c a t s FST for English Nominal Inflection +N: reg-n +PL:^s# q1 q4 +SG:# +N: irreg-n-sg q0 q2 q5 q7 +SG:# irreg-n-pl q3 q6 +PL:# +N:

  15. Useful Operations on Transducers • Cascade: running 2+ FSTs in sequence • Intersection: represent the common transitions in FST1 and FST2 (ASR: finding pronunciations) • Composition: apply FST2 transition function to result of FST1 transition function • Inversion: exchanging the input and output alphabets (recognize and generate with same FST) • cf AT&T FSM Toolkit and papers by Mohri, Pereira, and Riley

  16. Orthographic Rules and FSTs • Define additional FSTs to implement rules such as consonant doubling (beg begging), ‘e’ deletion (make  making), ‘e’ insertion (watch  watches), etc.

  17. 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

  18. Policy police • Does stemming help? • IR, little • Topic detection, more

  19. 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 5:1-8 • HW1 assigned (read the assignment)

More Related