# News blurb o the day Allied armed forces in Iraq using machine translation+AIM to communicate Many possible MT techniques; some based on Bayesian statistical.

## Presentation on theme: "News blurb o the day Allied armed forces in Iraq using machine translation+AIM to communicate Many possible MT techniques; some based on Bayesian statistical."— Presentation transcript:

News blurb o the day Allied armed forces in Iraq using machine translation+AIM to communicate Many possible MT techniques; some based on Bayesian statistical techniques Ex: see le chat noire the black cat; estimate Pr[black cat|chat noire] When you see chat next, estimate max probability word to associate with it Much more difficult than your spam filters -- need to handle entire phrases, words out of order, idom, etc.

Recursive Descent Parsing Or: Before you can understand this sentence, first, you must understand this sentence...

Recursive Descent Parsing A translation between streams of tokens and complex structures like trees (or tree-like data structs) One step beyond lexing Requires more sophisticated structures

Lexical analysis, revisited Rules equivalent to regular expressions Can only represent sequences, indefinite repetition (i.e., * or + operators), and finite cases ([] and | operators) Can be recognized in linear time Equivalent to a finite state machine

R.D. Parsing and CFGs Rules can be recursive Technically, based on context free grammars Needs a full stack machine, not just a state machine Stack can be unboundedly deep Needs more than a finite number of states to run

CFGs and BNF Write our rules in Bakus-Naur Normal Form (BNF) Rules made up of two elements: Terminals: actual tokens that could be found in the data -- dog, 127, {, [a-zA-Z]+ Non-terminals: names of rules Rules must be of form: LHS := term 1 op 1 term 2 op 2... term N op N LHS is a non-terminal term i is a terminal or non-terminal op i is one of the operators weve met before -- +, *, |, ()

BNF from P2 FILE := ( CONTROL | PUZZLEDEF )* CONTROL := ( OUTFILE | LOGFILE | ERRFILE | RESULTS | STATS | SEARCH-CTRL | "Run" | "Reset" )

BNF from P2 FILE := ( CONTROL | PUZZLEDEF )* CONTROL := ( OUTFILE | LOGFILE | ERRFILE | RESULTS | STATS | SEARCH-CTRL | "Run" | "Reset" )

BNF from P2 FILE := ( CONTROL | PUZZLEDEF )* CONTROL := ( OUTFILE | LOGFILE | ERRFILE | RESULTS | STATS | SEARCH-CTRL | "Run" | "Reset" )

Recursion... N2KPUZZLE := "NToTheKPuzzle" "(" HNAME ") "= "{ "StartState" "=" NKPUZSTATE "GoalState" "=" NKPUZSTATE "} NKPUZSTATE := "[ ( NUMLIST | NKPUZSTATE ( "," NKPUZSTATE )* ) "] NUMLIST := NON-NEG-INTEGER ( "," NON-NEG-INTEGER )* HNAME := [a-zA-Z]+ POS-INTEGER := [1-9][0-9]+ NON-NEG-INTEGER := [0-9]+

Turning it into code public PuzState parseNKPuzzle(Lexer l) { Token t=l.next(); if (!t.tokStr().equals(NToTheKPuzzle)) { throw new ParseException(Unexpected + token + t.tokStr() + found when expecting + N^k-1 puzzle state); } t=l.next(); if (!t.tokStr().equals(()) { //... } t=l.next(); if (t.getType()!=TT_HNAME) { //... } String heuristic=t.tokStr();

Turning it into code // parse ), =, {, StartState, // =. Now ready for NKPUZSTATE NkPuzStateRep sRep=parseNKPuzState(l); // now parse GoalState, = NkPuzStateRep gRep=parseNKPuzState(l); // parse } and you know youre done with // NKPUZ // now construct the actual puzzle object if (heuristic.equals(Manhattan) { NkPuz p=new NkManhattanPuz(sRep,gRep); return p; } if (heuristic.equals(TileCount) { NkPuz p=new NkTileCountPuz(sRep,gRep); return p; }

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