Artificial Intelligence: A Modern Approach

AIMA Python file: nlp.py

"""A chart parser and some grammars. (Chapter 22)"""

from utils import *


# Grammars and Lexicons def Rules(**rules): """Create a dictionary mapping symbols to alternative sequences. >>> Rules(A = "B C | D E") {'A': [['B', 'C'], ['D', 'E']]} """ for (lhs, rhs) in rules.items(): rules[lhs] = [alt.strip().split() for alt in rhs.split('|')] return rules def Lexicon(**rules): """Create a dictionary mapping symbols to alternative words. >>> Lexicon(Art = "the | a | an") {'Art': ['the', 'a', 'an']} """ for (lhs, rhs) in rules.items(): rules[lhs] = [word.strip() for word in rhs.split('|')] return rules class Grammar: def __init__(self, name, rules, lexicon): "A grammar has a set of rules and a lexicon." update(self, name=name, rules=rules, lexicon=lexicon) self.categories = DefaultDict([]) for lhs in lexicon: for word in lexicon[lhs]: self.categories[word].append(lhs) def rewrites_for(self, cat): "Return a sequence of possible rhs's that cat can be rewritten as." return self.rules.get(cat, ()) def isa(self, word, cat): "Return True iff word is of category cat" return cat in self.categories[word] def __repr__(self): return '<Grammar %s>' % self.name E0 = Grammar('E0', Rules( # Grammar for E_0 [Fig. 22.4] S = 'NP VP | S Conjunction S', NP = 'Pronoun | Noun | Article Noun | Digit Digit | NP PP | NP RelClause', VP = 'Verb | VP NP | VP Adjective | VP PP | VP Adverb', PP = 'Preposition NP', RelClause = 'That VP'), Lexicon( # Lexicon for E_0 [Fig. 22.3] Noun = "stench | breeze | glitter | nothing | wumpus | pit | pits | gold | east", Verb = "is | see | smell | shoot | fell | stinks | go | grab | carry | kill | turn | feel", Adjective = "right | left | east | south | back | smelly", Adverb = "here | there | nearby | ahead | right | left | east | south | back", Pronoun = "me | you | I | it", Name = "John | Mary | Boston | Aristotle", Article = "the | a | an", Preposition = "to | in | on | near", Conjunction = "and | or | but", Digit = "0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9", That = "that" )) E_ = Grammar('E_', # Trivial Grammar and lexicon for testing Rules( S = 'NP VP', NP = 'Art N | Pronoun', VP = 'V NP'), Lexicon( Art = 'the | a', N = 'man | woman | table | shoelace | saw', Pronoun = 'I | you | it', V = 'saw | liked | feel' )) def generate_random(grammar=E_, s='S'): """Replace each token in s by a random entry in grammar (recursively). This is useful for testing a grammar, e.g. generate_random(E_)""" import random def rewrite(tokens, into): for token in tokens: if token in grammar.rules: rewrite(random.choice(grammar.rules[token]), into) elif token in grammar.lexicon: into.append(random.choice(grammar.lexicon[token])) else: into.append(token) return into return ' '.join(rewrite(s.split(), []))
# Chart Parsing class
Chart: """Class for parsing sentences using a chart data structure. [Fig 22.7] >>> chart = Chart(E0); >>> len(chart.parses('the stench is in 2 2')) 1 """ def __init__(self, grammar, trace=False): """A datastructure for parsing a string; and methods to do the parse. self.chart[i] holds the edges that end just before the i'th word. Edges are 5-element lists of [start, end, lhs, [found], [expects]].""" update(self, grammar=grammar, trace=trace) def parses(self, words, S='S'): """Return a list of parses; words can be a list or string.""" if isinstance(words, str): words = words.split() self.parse(words, S) # Return all the parses that span the whole input return [[i, j, S, found, []] for (i, j, lhs, found, expects) in self.chart[len(words)] if lhs == S and expects == []] def parse(self, words, S='S'): """Parse a list of words; according to the grammar. Leave results in the chart.""" self.chart = [[] for i in range(len(words)+1)] self.add_edge([0, 0, 'S_', [], [S]]) for i in range(len(words)): self.scanner(i, words[i]) return self.chart def add_edge(self, edge): "Add edge to chart, and see if it extends or predicts another edge." start, end, lhs, found, expects = edge if edge not in self.chart[end]: self.chart[end].append(edge) if self.trace: print '%10s: added %s' % (caller(2), edge) if not expects: self.extender(edge) else: self.predictor(edge) def scanner(self, j, word): "For each edge expecting a word of this category here, extend the edge." for (i, j, A, alpha, Bb) in self.chart[j]: if Bb and self.grammar.isa(word, Bb[0]): self.add_edge([i, j+1, A, alpha + [(Bb[0], word)], Bb[1:]]) def predictor(self, (i, j, A, alpha, Bb)): "Add to chart any rules for B that could help extend this edge." B = Bb[0] if B in self.grammar.rules: for rhs in self.grammar.rewrites_for(B): self.add_edge([j, j, B, [], rhs]) def extender(self, edge): "See what edges can be extended by this edge." (j, k, B, _, _) = edge for (i, j, A, alpha, B1b) in self.chart[j]: if B1b and B == B1b[0]: self.add_edge([i, k, A, alpha + [edge], B1b[1:]]) #### TODO: #### 1. Parsing with augmentations -- requires unification, etc. #### 2. Sequitor

AI: A Modern Approach by Stuart Russell and Peter NorvigModified: Jul 18, 2005