1. 10. Parsing with Context-free Grammars -Speech and Language Processing- 발표자 : 정영임 발표일 : 2007. 8. 7.

2. 2 10.4 The Earley Algorithm Earley Algorithm  Dynamic programming  Solution for those three parsing problems  Information Represented by  Chart: N+1 entries (N: Number of words)  Dotted rule e.g.) S → VP [0,0]

3. 3 10.4 The Earley Algorithm Fig.10.16 The Earley algorighm

4. 4 10.4 The Earley Algorithm Predictor  To create new states representing top-down expectations  is applied to any state that has a non-terminal immediately to the right of its dot that is not a part-of-speech category  results in the creation of one new state for each alternative expansion of that non-terminal provided by the grammar  begins and ends at the point in the input where the generating state ends.  Example  S → VP, [0,0]Predictor

5. 5 10.4 The Earley Algorithm Scanner  is called to examine the input and incorporate a state corresponding to the prediction of a word with a particular part-of-speech into the chart.  is accomplished by creating a new state from the input state with the dot advanced over the predicted input category.  Example  VP → Verb NP, [0,0]  Scanner consults the current word in the input since the category following the dot is a part-of-speech.  It notes that book can be a verb, matching the expectation in the current state  This results in the creation of the new state VP → Verb NP, [0,1].  The new state is added to the chart entry that follows the one currently being processed

6. 6 10.4 The Earley Algorithm Completer  is applied to a state when its dot has reached the right end of the rule.  is to find, and advance, all previously created states that were looking for this grammatical category at this position in the input.  New states are then created by copying the older state, advancing the dot over the expected category, and installing the new state in the current chart entry.  Example  NP → Det Nominal [1,3]  Completer looks for states ending at 1 expecting an NP –VP → Verb NP, [0,1]  This results in the addition of a new complete state VP → Verb NP, [0,3]

7. 7 10.4 The Earley Algorithm Fig.10.17 An Example “Book that filght.”

8. 8 10.4 The Earley Algorithm Retrieving Parse Trees from a Chart  the version of the Earley algorithm is a recognizer not a parser.  valid sentences will leave the state S → α, [0,N] in the chart.  Extraction of individual parses from the chart  the representation of each state must be augmented with an additional field to store information about the completed states that generated its constituents.  change necessary is to have COMPLETER add a pointer to the older state onto a list of constituent-states for the new state.  following pointers starting with the state (or states) representing a complete S in the final chart entry.

9. 9 10.4 The Earley Algorithm Retrieving Parse Trees from a Chart 22 218 1914 18 23 22 21 19 18 14 8 10.18

10. 10 10.4 The Earley Algorithm Cost at tree retrieval process  if there are an exponential number of trees for a given sentence, the algorithm will require an exponential amount of time to return them all. The Earley algorithm may fill the table in O(N 3 ) time but it can’t magically return them as quickly.

11. 11 10.5 Finite-State Parsing Methods Efficient in a partial parse or shallow parse  Recognition of basic phrases(noun groups, verb groups, location, preposition and etc.)  Extraction of some sort of template in required data

12. 12 10.5 Finite-State Parsing Methods Finite-state rules for detecting noun groups(NG)  NG → Pronoun|Time-NP|Date-NP  NG → (DETP)(Adjs) HdNns|DETP Ving HdNns|DETP-CP (and HdNns)  DETP → DETP-CP|DETP-INCP  DETP-CP → ({Adv-pre-num|“another”|{Det|Pro-Poss}({Adv-pre- num|“only”(“other”)})})Number|Q|Q-er|(“the”)Q-est| “another”|Det- cp|DetQ|Pro-Poss-cp  DETP-INCP {{{Det|Pro-Poss}|“only”|“a”|“an”|Det-incomp|Pro-Poss- incomp}(“other”)|(DET-CP)“other”}  Adjs → AdjP({ “,”|(“,”) Conj}{AdjP|Vparticiple})*  AdjP → Ordinal|{(Q-er|Q-est}{Adj|Vparticiple}+|Number(“-”){“month”| “day” | “year”}(“-”) “old”}

13. 13 10.5 Finite-State Parsing Methods Finite-state rules for detecting noun groups(NG) (Ctnd’)  HdNns -> HdNn(“and” HdNn)  HdNn -> PropN|{PreNs|PropNPreNs}N[!Time-NP] |{PropN CommonN[!Time-NP]}  PreNs -> PreN(“and” PreN2)*  PreN -> (Adj”-”)Common-Sing-N  PreN2 -> PreN|Ordinal|Adj-noun-like

14. 14 10.5 Finite-State Parsing Methods Fig. 10.20-10.21

15. 15 10.5 Finite-State Parsing Methods Handling recursion of complete English grammar  Allowing only a limited amount of recursion  FASTUS does this by using its automata cascade  The second level of FASTUS finds non-recursive noun group  The third level combines these groups into larger NP-like units by –adding on measure phrases »20,000 iron and “metal wood” clubs a month –Attaching preposition phrases »Production of 20,000 iron and “metal wood” … –Dealing with noun group conjunction »A local concern and a Japanese trading house => By splitting the parsing into two levels, NP on the left side is treated as a different kind of object from NP on the right side

16. 16 10.5 Finite-State Parsing Methods Handling recursion of complete English grammar  Chunk-based partial parsing via a set of finite-set cascades(Abney, 1996)

17. 17 10.5 Finite-State Parsing Methods Handling recursion of complete English grammar  Recursive Transition Network(RTN)  RTN is defined by a set of graphs like those in Fig.10.20 and Fig. 10.21  Each arc contains a terminal or non-terminal node  Difference between RTN and FSA –In an RTN, whenever the machine comes to an arc labeled with a non-terminals, it treats that non-terminal as a subroutine »It places its current location onto a stack »It jumps to the non-terminal »Then it jumps back when that non-terminal has been parsed  RTN is exactly equivalent to a context-free grammar –A graphical way to view a simple top-down parser for context- free rules