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yap-6.3/packages/prism/exs/pdcg_c.psm
2011-11-10 12:24:47 +00:00

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%%%%
%%%% Probabilistic DCG for Charniak's example --- pdcg_c.psm
%%%%
%%%% Copyright (C) 2007,2008
%%%% Sato Laboratory, Dept. of Computer Science,
%%%% Tokyo Institute of Technology
%% As described in the comments in pdcg.psm, PCFGs (probabilistic context-
%% free grammars) are a stochastic extension of CFG grammar such that in a
%% (leftmost) derivation, each production rule is selected probabilistically
%% and applied. This program presents an implementation of an example from
%% Charniak's textbook (Statistical Language Learning, The MIT Press, 1993):
%%
%% s --> np vp (0.8) | verb --> swat (0.2)
%% s --> vp (0.2) | verb --> flies (0.4)
%% np --> noun (0.4) | verb --> like (0.4)
%% np --> noun pp (0.4) | noun --> swat (0.05)
%% np --> noun np (0.2) | noun --> flies (0.45)
%% vp --> verb (0.3) | noun --> ants (0.5)
%% vp --> verb np (0.3) | prep --> like (1.0)
%% vp --> verb pp (0.2) |
%% vp --> verb np pp (0.2) |
%% pp --> prep np (1.0) |
%% (`s' is the start symbol)
%%
%% This program has a grammar-independent part (pcfg/1-2 and proj/2),
%% which can work with any underlying CFG which has no epsilon rules
%% and produces no unit cycles.
%%----------------------------------
%% Quick start:
%%
%% ?- prism(pdcg_c).
%%
%% ?- prob(pcfg([swat,flies,like,ants])).
%% % get the generative probability of a sentence
%% % "swat flies like ants"
%%
%% ?- sample(pcfg(_X)),viterbif(pcfg(_X)).
%% % parse a sampled sentence
%%
%% ?- get_samples(50,pcfg(X),_Gs),learn(_Gs),show_sw.
%% % conduct an artificial learning experiments
%%
%% ?- viterbif(pcfg([swat,flies,like,ants])).
%% % get the most probabile parse for "swat flies like ants"
%%
%% ?- n_viterbif(3,pcfg([swat,flies,like,ants])).
%% % get top 3 ranked parses for "swat flies like ants"
%%
%% ?- viterbit(pcfg([swat,flies,like,ants])).
%% % print the most probabile parse for "swat flies like ants" in
%% % a tree form.
%%
%% ?- viterbit(pcfg([swat,flies,like,ants]),P,E), build_tree(E,T).
%% % get the most probabile parse for "swat flies like ants" in a
%% % tree form, and convert it to a more readable Prolog term.
%%
%% ?- probfi(pcfg([swat,flies,like,ants])).
%% % print the parse forest with inside probabilities
%%
%%----------------------------------
%% Declarations:
values(s,[[np,vp],[vp]]).
values(np,[[noun],[noun,pp],[noun,np]]).
values(vp,[[verb],[verb,np],[verb,pp],[verb,np,pp]]).
values(pp,[[prep,np]]).
values(verb,[[swat],[flies],[like]]).
values(noun,[[swat],[flies],[ants]]).
values(prep,[[like]]).
:- p_not_table proj/2. % This declaration is introduced just for
% making the results of probabilistic inferences
% simple and readable.
%%----------------------------------
%% Modeling part:
pcfg(L):- pcfg(s,L-[]).
pcfg(LHS,L0-L1):-
( nonterminal(LHS) -> msw(LHS,RHS),proj(RHS,L0-L1)
; L0 = [LHS|L1]
).
proj([],L-L).
proj([X|Xs],L0-L1):-
pcfg(X,L0-L2),proj(Xs,L2-L1).
nonterminal(s).
nonterminal(np).
nonterminal(vp).
nonterminal(pp).
nonterminal(verb).
nonterminal(noun).
nonterminal(prep).
%%----------------------------------
%% Utility part:
% set the rule probabilities:
:- set_sw(s,[0.8,0.2]).
:- set_sw(np,[0.4,0.4,0.2]).
:- set_sw(vp,[0.3,0.3,0.2,0.2]).
:- set_sw(pp,[1.0]).
:- set_sw(verb,[0.2,0.4,0.4]).
:- set_sw(noun,[0.05,0.45,0.5]).
:- set_sw(prep,[1.0]).
% build_tree(E,T):-
% Build a parse tree T from a tree-formed explanation E.
build_tree([],[]).
build_tree([pcfg(_),Gs],T) :- build_tree(Gs,T).
build_tree([pcfg(Sym,_)|Gs],T) :- build_tree1(Gs,T0),T=..[Sym|T0].
build_tree1([],[]).
build_tree1([pcfg(Sym,_)|Gs],[Sym|T]) :- !,build_tree1(Gs,T).
build_tree1([msw(_,_)|Gs],T) :- !, build_tree1(Gs,T).
build_tree1([G|Gs],[T0|T]) :- build_tree(G,T0),!,build_tree1(Gs,T).