Linguistics 239E Week 6

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Linguistics 239E Week 6
FSTs and XLE Grammars

• Ron Kaplan and Tracy King

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NLP Reading Group

• Weekly meetings, Fri 200 - 315, Ventura 17
• This week
• S. Bilac, T. Baldwin, and H. Tanaka. 2003.
  Improving dictionary accessibility by maximizing
  use of available knowledge
• URL http //nlp.stanford.edu/nlp/nlpgroup.html
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Office Hours/Homework

• Monday is a holiday
• Office hours Tuesday 4.00-4.30
• HW assignment will not be posted until tomorrow
  afternoon

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Issues from Week 5 Homework

• "Useless existential constraint"
• S --gt NP ( SUBJ)!
• VP.
• Context free parsing
• adding the constraints cuts down on the number of
  final analyses
• the trees are all still there
• to eliminate trees, you would have to change the
  c-structure in some way

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Issues from Week 5 Homework

• Double analysis with "want to"
• separate out the paths in the functional
  uncertainty so that SUBJ and XCOMP SUBJ do not
  show up together (hard to do in general case)
• off-path constraint
• ( COMPXCOMP SUBJ (lt- XCOMP) OBJ
  OBL OBJ)!
• subtract out the XCOMP SUBJ (recommended
  solution)
• ( COMPXCOMP SUBJ OBJ OBL OBJ
  ? XCOMP SUBJ )!

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Issues from Week5 Homework

• "gap" to create the gap, the constituent in
  question can just be optional e is not needed
• (NP ( OBJ)!)
• NP ( OBJ)!
• e
• Analysis of "be" (tangential to assignment)
• "be" should have two arguments
• it is unclear if the second argument is "open"
  (XCOMP) or "closed" (OBJ/PREDLINK)

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FSTs in XLE grammars

• FSTs are used for
• tokenization
• morphological analysis
• Incorporated via the MORPHCONFIG

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Tokenization

• Tokenization breaks up a string (sentence) into
  tokens (words)
• Break off punctuation
• Break off clitics
• Lowercasing
• Allow for markup

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Punctuation and clitics

• Simple breaking
• I see them. gt I TB see TB them TB . TB
• The dog, a poodle, gt The TB dog TB , TB a TB
  poodle TB , TB
• Haplology
• Find the dog, Muffy. gt Find TB the TB dog TB ,
  TB Muffy TB , TB . TB
• Go to Palm Dr. gt Go TB to TB Palm TB Dr. TB .
  TB
• Clitics
• Ill go. gt I TB ll TB go TB . TB

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Punctuation Problems

• When to break off punctuation is not always clear
• Hyphens part of the word or separate
  punctuation?
• a six-year-old boy
• a windshield-wiper blade cleaner
• The dog - a poodle - barked.

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Lowercasing

• Need to (optionally) lowercase in certain
  positions (depends on the language)
• Sentence initially
• The boy left. gt the boy left.
• Mary left. gt Mary left.
• After colons
• The boy left He was unhappy. gt The boy left
  he was unhappy.
• All caps
• Do NOT leave. gt do not leave.
• IBM did well. gt IBM did well.

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Tokenizers are non-deterministic

• Allow for multiple tokenizations to guarantee
  correct one
• Bush saw them. gt Bush bush TB saw TB them
  TB , TB . TB
• May include markup
• All caps lowering marked
• IBM gt IBM ibm

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Allowing for markup

• Normal rules of tokenization (lowercasing,
  haplology) need to skip markup
• The markup should not be broken up like regular
  punctuation
• labeled bracketing
• I see \NP the dog, a poodle\.
• named entities
• ltpersongtMr. Smithlt/persongt

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FST Morphologies

• Associate a surface form of a word with a
  canonical form (lemma, stem) and a set of tags
• Tags give grammatical information
• Part of speech
• Other information (number, tense, etc)
• Tags may give additional information
• Classes of proper nouns (names, locations)

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Examples English

• went go "Verb" "PastTense" "123SP"
• boxes box "Noun" "Pl"
• "Verb" "Pres" "3sg"
• Mary "Prop" "Giv" "Fem" "Sg"
• him he "Pron" "Pers" "Acc" "3P" "Sg"

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Examples French

• fleur fleur "Fem" "SG" "Noun"
• venir venir "Inf" "Verb"
• vienne venir "SubjP" "SG" "P1""P3"
  "Verb"
• tour tour "Masc""Fem" "SG" "Noun"
• France France "Fem" "InvPL" "Country"
  "Proper" "Noun"

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An example

• String Children came.
• Tokens Children children TB came TB ,
  TB . TB
• Morphology (for the tokens we want)
• child Noun Pl children Token
• come Verb PastTense 123SP came
  Token
• . Punct Sent . Token
• Outputs from tokenizer and morphology fsts can
  multiply out

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The process in XLE
XLE words
words
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Viewing the analysis in XLE

• If a FST tokenizer is loaded with the grammar
• tokens Ill try this string.
• If a FST morphology is loaded with the grammar
• morphemes testing
• These results are also visible in the morph
  window (from the c-structure window options)

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Using FSTs with the grammar

• Tokenize the string
• Children came. gt children TB came TB . TB
• Run the tokens through the morphology
• child Noun Pl come Verb PastTense 123SP .
  Punct Sent
• Parse the lemmas and the tags
• sublexical rules build up the words
• regular rules build the words into phrases
• each tag has a lexical entry

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Lexical entries for stems and tags

• Like the lexical entries you have seen, only with
  XLE instead of
• boy N XLE _at_(NOUN boy).
• Noun N_SFX XLE _at_(PERS 3).
• Sg N_NUM XLE _at_(NUM sg).
• Pl N_NUM XLE _at_(NUM pl).
• Note no entry for boys
• matches tokens that dont go through FST, XLE
  matches FST output stems

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Sublexical rules

• Want to insert rules between the lexical
  categories (e.g. N) and the same category in the
  lexicon
• But the lexical category only identifies the stem
  or base
• Sublexical rules combine the base with the
  inflectional tags
• So, build a category (N) from the base (N_BASE)

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Sublexical rules cont.

• Like lexical rules only
• Add _BASE to the category in the lexicon
• boy N Noun N_SFX Sg N_NUM
• Example
• N --gt N_BASE
• N_SFX_BASE
• N_NUM_BASE.
• When parsing, the sublexical trees are not shown.
  Right click on the leave node (e.g., N) and
  choose "show morphemes" to see them.

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NP example tree
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Sublexical rules cont.

• A --gt A_BASE
• A_SFX_BASE
• (A_DEG_BASE). optionality
• N --gt N_BASE disjunction
• N_SFX_BASE
• N_NUM_BASE
• VN_BASE
• V_SFX_BASE
• V_TYPE_BASE. kleene star

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Using the -unknown entry

• Words with predictable subcat frames can go
  through the special entry -unknown
• The tags will constrain the distribution
• This avoids having to list all adverbs,
  adjectives, nouns, etc.
• stem picks up the lemma/stem
• -unknown ADJ XLE _at_(ADJ stem)
• N XLE _at_(NOUN stem)
• ADV XLE _at_(ADVERB stem).

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Lexicon and -unknown

• Verbs ought to be listed due to their subcat
  frames
• Idiosyncratic entries for nouns, etc. need to be
  listed
• But, avoid duplicating the word done by the FST
  morphology in the lexicon--mapping to categories
  done in only one place

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FST guessers

• The morphologies are good, but dont have all
  words
• FST guessers can be written
• work best for languages with lots of morphology
• for English
• -ed can be a verb or adjective
• -ing can be a verb, noun, or adjective
• -s can be a plural noun or 3sg verb
• words starting with capitals can be proper nouns
• etc.

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Using multiple FSTs

• How FSTs are used is declared in the MORPHCONFIG
• The toy grammars use a default MORPHCONFIG
• TOKENIZE and ANALYZE sections
• Sections to specify
• where the fsts are
• how to treat multiword expressions

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Example MORPHCONFIG

• STANDARD ENGLISH MORPHOLOGY (1.0)
• TOKENIZE
• whitespace.fst tokenizer.fst
• ANALYZE USEFIRST
• main-morphology.fst
• english-guesser.fst
• ANALYZE USEALL
• eureka-numbers.fst
• eureka-novel-nouns.txt
• ----

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Morphconfig cont.

• TOKENIZE
• whitespace.fst tokenizer.fst
• The fsts listed are composed output of first is
  input to second, etc.
• Having multiple fsts
• may avoid problems with large
  compositions
• allows for modularity

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Morphconfig cont.

• ANALYZE USEFIRST
• main-morphology.fst
• english-guesser.fst
• Take as input the individual tokens from the
  tokenizer
• Apply the analyzers one by one until an analysis
  is found. Once an analysis is found, it stops.
• Effect of the above example
• first try to find the analysis in the main
  morphology
• if that fails, guess the morphological analysis

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Morphconfig cont.

• ANALYZE USEALL
• eureka-numbers.fst
• eureka-novel-nouns.fst
• Each morphological analyzer is applied to the
  string, produces union of results
• In the example, if a string could be both a
  eureka number and a eureka novel noun, it will
  get both analyses
• It is not necessary to have both USEALL and
  USEFIRST sections.

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FST/XLE main points

• XLE allows the incorporation of FSTS through the
  MORPHCONFIG
• Tokenizers, including special markup, and
  morphological analyzers can be included
• Large morphological analyzers in conjunction with
  sublexical rules and the unknown lexical item
  reduce the need for lexicon development

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Demo from the large English grammar

• tokens and morphemes commands
• morphology window
• sublexical rules
• guesser

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