Parsing: computing the grammatical structure of English sentences

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Parsing computing the grammatical structure of
English sentences
School of Computing FACULTY OF ENGINEERING

• COMP3310 Natural Language Processing
• Eric Atwell, Language Research Group
• (with thanks to Katja Markert, Marti Hearst, and
  other contributors)

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Reminder Outline for Grammar/Parsing

• Context-Free Grammars and Constituency
• Some common CFG phenomena for English
• Sentence-level constructions
• NP, PP, VP
• Coordination
• Subcategorization
• Top-down and Bottom-up Parsing
• Chart Parsing

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CFG example

• S -gt NP VP
• NP -gt Det NOMINAL
• NOMINAL -gt Noun
• VP -gt Verb
• Det -gt a
• Noun -gt flight
• Verb -gt left

• Alternatively
• S -gt NP VP
• NP -gt A N
• VP -gt V
• A -gt a
• N -gt flight
• V -gt left

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Derivations

• A derivation is a sequence of rules applied to a
  string that accounts for that string
• Covers all the elements in the string
• Covers only the elements in the string

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Bracketed Notation

• S NP PRO I VP V prefer NP Det a Nom
  N morning
• N flight

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CFGs a summary

• CFGs appear to be just about what we need to
  account for a lot of basic syntactic structure in
  English.
• But there are problems
• That can be dealt with adequately, although not
  elegantly, by staying within the CFG framework.
• There are simpler, more elegant, solutions that
  take us out of the CFG framework (beyond its
  formal power).
• Syntactic theories TG, HPSG, LFG, GPSG, etc.

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Other syntactic stuff

• Grammatical relations or functions
• Subject
• I booked a flight to New York
• The flight was booked by my agent
• Object
• I booked a flight to New York
• Complement
• I said that I wanted to leave

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Dependency parsing

• Word to word links instead of constituency
• Based on the European rather than American
  traditions
• But dates back to ancient Greek and Arab scholars
• Eg see Quranic Arabic Corpus
• The original notions of Subject, Object and the
  progenitor of subcategorization (called
  valence) came out of Dependency theory.
• Dependency parsing is quite popular as a
  computational model
• since relationships between words are quite
  useful

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Dependency parsing
Parse tree Nesting of multi-word constituents
Typed dep parse Grammatical relations between
individual words
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Why are dependency parses useful?

• Example multi-document summarization
• Need to identify sentences from different
  documents that each say roughly the same thing
• phrase structure trees of paraphrasing sentences
  which differ in word order can be significantly
  different
• but dependency representations will be very
  similar

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Parsing

• Parsing assigning correct trees to input strings
• Correct tree a tree that covers all and only the
  elements of the input and has an S at the top
• For now enumerate all possible trees
• A further task disambiguation means choosing
  the correct tree from among all the possible
  trees.

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Treebanks

• Parsed corpora in the form of trees
• The Penn Treebank
• The Brown corpus
• The WSJ corpus
• Tgrep
• http//www.ldc.upenn.edu/ldc/online/treebank/
• Tregex
• http//www-nlp.stanford.edu/nlp/javadoc/javanlp/

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Parsing involves search

• As with everything of interest, parsing involves
  a search which involves the making of choices
• Well start with some basic (meaning bad) methods
  before moving on to the one or two that you need
  to know

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For Now

• Assume
• You have all the words already in some buffer
• The input isnt pos tagged
• We wont worry about morphological analysis
• All the words are known

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Top-Down Parsing

• Since were trying to find trees rooted with an S
  (Sentences) start with the rules that give us an
  S.
• Then work your way down from there to the words.

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Top Down Space
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Bottom-Up Parsing

• Of course, we also want trees that cover the
  input words. So start with trees that link up
  with the words in the right way.
• Then work your way up from there.

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Bottom-Up Space
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Control

• Of course, in both cases we left out how to keep
  track of the search space and how to make choices
• Which node to try to expand next
• Which grammar rule to use to expand a node

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Top-Down, Depth-First, Left-to-Right Search
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Example
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Example
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Example
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Top-Down and Bottom-Up

• Top-down
• Only searches for trees that can be answers (i.e.
  Ss)
• But also suggests trees that are not consistent
  with the words
• Bottom-up
• Only forms trees consistent with the words
• Suggest trees that make no sense globally

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So Combine Them

• There are a million ways to combine top-down
  expectations with bottom-up data to get more
  efficient searches
• Most use one kind as the control and the other as
  a filter
• As in top-down parsing with bottom-up filtering

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Adding Bottom-Up Filtering
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3 problems with TDDFLtR Parser

• Left-Recursion
• Ambiguity
• Inefficient reparsing of subtrees

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Left-Recursion

• What happens in the following situation
• S -gt NP VP
• S -gt Aux NP VP
• NP -gt NP PP
• NP -gt Det Nominal
• With the sentence starting with
• Did the flight

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Ambiguity

• One morning I shot an elephant in my pajamas.
  How he got into my pajamas I dont know.
• Groucho Marx

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Lots of ambiguity

• VP -gt VP PP
• NP -gt NP PP
• Show me the meal on flight 286 from SF to Denver
• 14 parses!

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Lots of ambiguity

• Church and Patil (1982)
• Number of parses for such sentences grows at rate
  of number of parenthesizations of arithmetic
  expressions
• Which grow with Catalan numbers

• PPs Parses
• 1 2
• 2 5
• 3 14
• 4 132
• 5 469
• 6 1430

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Chart ParserAvoiding Repeated Work

• Parsing is hard, and slow. Its wasteful to redo
  stuff over and over and over.
• A CHART PARSER maintains a CHART a table of
  partial parses found so far, to re-use if
  required.
• Consider an attempt to top-down parse the
  following as an NP
• A flight from Indianapolis to Houston on TWA

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Grammars and Parsing

• Context-Free Grammars and Constituency
• Some common CFG phenomena for English
• Basic parsers Top-down and Bottom-up Parsing
• Chart Parser keep a CHART of partial parses