835 lines
30 KiB
Python
835 lines
30 KiB
Python
# Natural Language Toolkit: Glue Semantics
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#
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# Author: Dan Garrette <dhgarrette@gmail.com>
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#
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# Copyright (C) 2001-2019 NLTK Project
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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import os
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from itertools import chain
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from six import string_types
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import nltk
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from nltk.internals import Counter
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from nltk.tag import UnigramTagger, BigramTagger, TrigramTagger, RegexpTagger
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from nltk.sem.logic import (
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Expression,
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Variable,
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VariableExpression,
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LambdaExpression,
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AbstractVariableExpression,
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)
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from nltk.sem import drt
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from nltk.sem import linearlogic
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SPEC_SEMTYPES = {
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"a": "ex_quant",
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"an": "ex_quant",
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"every": "univ_quant",
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"the": "def_art",
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"no": "no_quant",
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"default": "ex_quant",
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}
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OPTIONAL_RELATIONSHIPS = ["nmod", "vmod", "punct"]
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class GlueFormula(object):
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def __init__(self, meaning, glue, indices=None):
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if not indices:
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indices = set()
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if isinstance(meaning, string_types):
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self.meaning = Expression.fromstring(meaning)
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elif isinstance(meaning, Expression):
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self.meaning = meaning
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else:
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raise RuntimeError(
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"Meaning term neither string or expression: %s, %s"
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% (meaning, meaning.__class__)
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)
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if isinstance(glue, string_types):
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self.glue = linearlogic.LinearLogicParser().parse(glue)
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elif isinstance(glue, linearlogic.Expression):
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self.glue = glue
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else:
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raise RuntimeError(
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"Glue term neither string or expression: %s, %s"
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% (glue, glue.__class__)
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)
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self.indices = indices
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def applyto(self, arg):
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""" self = (\\x.(walk x), (subj -o f))
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arg = (john , subj)
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returns ((walk john), f)
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"""
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if self.indices & arg.indices: # if the sets are NOT disjoint
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raise linearlogic.LinearLogicApplicationException(
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"'%s' applied to '%s'. Indices are not disjoint." % (self, arg)
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)
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else: # if the sets ARE disjoint
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return_indices = self.indices | arg.indices
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try:
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return_glue = linearlogic.ApplicationExpression(
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self.glue, arg.glue, arg.indices
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)
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except linearlogic.LinearLogicApplicationException:
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raise linearlogic.LinearLogicApplicationException(
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"'%s' applied to '%s'" % (self.simplify(), arg.simplify())
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)
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arg_meaning_abstracted = arg.meaning
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if return_indices:
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for dep in self.glue.simplify().antecedent.dependencies[
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::-1
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]: # if self.glue is (A -o B), dep is in A.dependencies
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arg_meaning_abstracted = self.make_LambdaExpression(
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Variable("v%s" % dep), arg_meaning_abstracted
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)
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return_meaning = self.meaning.applyto(arg_meaning_abstracted)
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return self.__class__(return_meaning, return_glue, return_indices)
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def make_VariableExpression(self, name):
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return VariableExpression(name)
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def make_LambdaExpression(self, variable, term):
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return LambdaExpression(variable, term)
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def lambda_abstract(self, other):
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assert isinstance(other, GlueFormula)
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assert isinstance(other.meaning, AbstractVariableExpression)
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return self.__class__(
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self.make_LambdaExpression(other.meaning.variable, self.meaning),
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linearlogic.ImpExpression(other.glue, self.glue),
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)
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def compile(self, counter=None):
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"""From Iddo Lev's PhD Dissertation p108-109"""
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if not counter:
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counter = Counter()
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(compiled_glue, new_forms) = self.glue.simplify().compile_pos(
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counter, self.__class__
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)
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return new_forms + [
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self.__class__(self.meaning, compiled_glue, set([counter.get()]))
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]
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def simplify(self):
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return self.__class__(
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self.meaning.simplify(), self.glue.simplify(), self.indices
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)
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def __eq__(self, other):
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return (
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self.__class__ == other.__class__
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and self.meaning == other.meaning
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and self.glue == other.glue
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)
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def __ne__(self, other):
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return not self == other
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# sorting for use in doctests which must be deterministic
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def __lt__(self, other):
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return str(self) < str(other)
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def __str__(self):
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assert isinstance(self.indices, set)
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accum = "%s : %s" % (self.meaning, self.glue)
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if self.indices:
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accum += " : {" + ", ".join(str(index) for index in self.indices) + "}"
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return accum
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def __repr__(self):
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return "%s" % self
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class GlueDict(dict):
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def __init__(self, filename, encoding=None):
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self.filename = filename
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self.file_encoding = encoding
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self.read_file()
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def read_file(self, empty_first=True):
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if empty_first:
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self.clear()
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try:
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contents = nltk.data.load(
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self.filename, format="text", encoding=self.file_encoding
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)
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# TODO: the above can't handle zip files, but this should anyway be fixed in nltk.data.load()
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except LookupError as e:
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try:
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contents = nltk.data.load(
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"file:" + self.filename, format="text", encoding=self.file_encoding
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)
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except LookupError:
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raise e
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lines = contents.splitlines()
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for line in lines: # example: 'n : (\\x.(<word> x), (v-or))'
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# lambdacalc -^ linear logic -^
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line = line.strip() # remove trailing newline
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if not len(line):
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continue # skip empty lines
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if line[0] == "#":
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continue # skip commented out lines
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parts = line.split(
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" : ", 2
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) # ['verb', '(\\x.(<word> x), ( subj -o f ))', '[subj]']
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glue_formulas = []
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paren_count = 0
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tuple_start = 0
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tuple_comma = 0
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relationships = None
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if len(parts) > 1:
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for (i, c) in enumerate(parts[1]):
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if c == "(":
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if paren_count == 0: # if it's the first '(' of a tuple
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tuple_start = i + 1 # then save the index
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paren_count += 1
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elif c == ")":
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paren_count -= 1
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if paren_count == 0: # if it's the last ')' of a tuple
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meaning_term = parts[1][
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tuple_start:tuple_comma
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] # '\\x.(<word> x)'
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glue_term = parts[1][tuple_comma + 1 : i] # '(v-r)'
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glue_formulas.append(
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[meaning_term, glue_term]
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) # add the GlueFormula to the list
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elif c == ",":
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if (
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paren_count == 1
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): # if it's a comma separating the parts of the tuple
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tuple_comma = i # then save the index
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elif c == "#": # skip comments at the ends of lines
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if (
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paren_count != 0
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): # if the line hasn't parsed correctly so far
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raise RuntimeError(
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"Formula syntax is incorrect for entry " + line
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)
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break # break to the next line
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if len(parts) > 2: # if there is a relationship entry at the end
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rel_start = parts[2].index("[") + 1
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rel_end = parts[2].index("]")
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if rel_start == rel_end:
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relationships = frozenset()
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else:
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relationships = frozenset(
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r.strip() for r in parts[2][rel_start:rel_end].split(",")
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)
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try:
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start_inheritance = parts[0].index("(")
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end_inheritance = parts[0].index(")")
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sem = parts[0][:start_inheritance].strip()
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supertype = parts[0][start_inheritance + 1 : end_inheritance]
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except:
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sem = parts[0].strip()
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supertype = None
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if sem not in self:
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self[sem] = {}
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if (
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relationships is None
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): # if not specified for a specific relationship set
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# add all relationship entries for parents
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if supertype:
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for rels in self[supertype]:
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if rels not in self[sem]:
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self[sem][rels] = []
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glue = self[supertype][rels]
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self[sem][rels].extend(glue)
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self[sem][rels].extend(
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glue_formulas
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) # add the glue formulas to every rel entry
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else:
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if None not in self[sem]:
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self[sem][None] = []
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self[sem][None].extend(
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glue_formulas
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) # add the glue formulas to every rel entry
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else:
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if relationships not in self[sem]:
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self[sem][relationships] = []
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if supertype:
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self[sem][relationships].extend(self[supertype][relationships])
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self[sem][relationships].extend(
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glue_formulas
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) # add the glue entry to the dictionary
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def __str__(self):
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accum = ""
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for pos in self:
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str_pos = "%s" % pos
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for relset in self[pos]:
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i = 1
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for gf in self[pos][relset]:
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if i == 1:
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accum += str_pos + ": "
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else:
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accum += " " * (len(str_pos) + 2)
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accum += "%s" % gf
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if relset and i == len(self[pos][relset]):
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accum += " : %s" % relset
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accum += "\n"
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i += 1
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return accum
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def to_glueformula_list(self, depgraph, node=None, counter=None, verbose=False):
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if node is None:
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# TODO: should it be depgraph.root? Is this code tested?
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top = depgraph.nodes[0]
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depList = list(chain(*top["deps"].values()))
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root = depgraph.nodes[depList[0]]
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return self.to_glueformula_list(depgraph, root, Counter(), verbose)
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glueformulas = self.lookup(node, depgraph, counter)
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for dep_idx in chain(*node["deps"].values()):
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dep = depgraph.nodes[dep_idx]
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glueformulas.extend(
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self.to_glueformula_list(depgraph, dep, counter, verbose)
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)
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return glueformulas
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def lookup(self, node, depgraph, counter):
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semtype_names = self.get_semtypes(node)
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semtype = None
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for name in semtype_names:
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if name in self:
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semtype = self[name]
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break
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if semtype is None:
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# raise KeyError, "There is no GlueDict entry for sem type '%s' (for '%s')" % (sem, word)
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return []
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self.add_missing_dependencies(node, depgraph)
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lookup = self._lookup_semtype_option(semtype, node, depgraph)
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if not len(lookup):
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raise KeyError(
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"There is no GlueDict entry for sem type of '%s' "
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"with tag '%s', and rel '%s'" % (node["word"], node["tag"], node["rel"])
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)
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return self.get_glueformulas_from_semtype_entry(
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lookup, node["word"], node, depgraph, counter
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)
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def add_missing_dependencies(self, node, depgraph):
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rel = node["rel"].lower()
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if rel == "main":
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headnode = depgraph.nodes[node["head"]]
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subj = self.lookup_unique("subj", headnode, depgraph)
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relation = subj["rel"]
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node["deps"].setdefault(relation, [])
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node["deps"][relation].append(subj["address"])
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# node['deps'].append(subj['address'])
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def _lookup_semtype_option(self, semtype, node, depgraph):
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relationships = frozenset(
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depgraph.nodes[dep]["rel"].lower()
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for dep in chain(*node["deps"].values())
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if depgraph.nodes[dep]["rel"].lower() not in OPTIONAL_RELATIONSHIPS
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)
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try:
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lookup = semtype[relationships]
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except KeyError:
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# An exact match is not found, so find the best match where
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# 'best' is defined as the glue entry whose relationship set has the
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# most relations of any possible relationship set that is a subset
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# of the actual depgraph
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best_match = frozenset()
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for relset_option in set(semtype) - set([None]):
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if (
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len(relset_option) > len(best_match)
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and relset_option < relationships
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):
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best_match = relset_option
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if not best_match:
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if None in semtype:
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best_match = None
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else:
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return None
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lookup = semtype[best_match]
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return lookup
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def get_semtypes(self, node):
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"""
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Based on the node, return a list of plausible semtypes in order of
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plausibility.
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"""
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rel = node["rel"].lower()
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word = node["word"].lower()
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if rel == "spec":
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if word in SPEC_SEMTYPES:
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return [SPEC_SEMTYPES[word]]
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else:
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return [SPEC_SEMTYPES["default"]]
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elif rel in ["nmod", "vmod"]:
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return [node["tag"], rel]
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else:
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return [node["tag"]]
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def get_glueformulas_from_semtype_entry(
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self, lookup, word, node, depgraph, counter
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):
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glueformulas = []
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glueFormulaFactory = self.get_GlueFormula_factory()
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for meaning, glue in lookup:
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gf = glueFormulaFactory(self.get_meaning_formula(meaning, word), glue)
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if not len(glueformulas):
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gf.word = word
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else:
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gf.word = "%s%s" % (word, len(glueformulas) + 1)
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gf.glue = self.initialize_labels(gf.glue, node, depgraph, counter.get())
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glueformulas.append(gf)
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return glueformulas
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def get_meaning_formula(self, generic, word):
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"""
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:param generic: A meaning formula string containing the
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parameter "<word>"
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:param word: The actual word to be replace "<word>"
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"""
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word = word.replace(".", "")
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return generic.replace("<word>", word)
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def initialize_labels(self, expr, node, depgraph, unique_index):
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if isinstance(expr, linearlogic.AtomicExpression):
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name = self.find_label_name(expr.name.lower(), node, depgraph, unique_index)
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if name[0].isupper():
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return linearlogic.VariableExpression(name)
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else:
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return linearlogic.ConstantExpression(name)
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else:
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return linearlogic.ImpExpression(
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self.initialize_labels(expr.antecedent, node, depgraph, unique_index),
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self.initialize_labels(expr.consequent, node, depgraph, unique_index),
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)
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def find_label_name(self, name, node, depgraph, unique_index):
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try:
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dot = name.index(".")
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before_dot = name[:dot]
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after_dot = name[dot + 1 :]
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if before_dot == "super":
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return self.find_label_name(
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after_dot, depgraph.nodes[node["head"]], depgraph, unique_index
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)
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else:
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return self.find_label_name(
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after_dot,
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self.lookup_unique(before_dot, node, depgraph),
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depgraph,
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unique_index,
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)
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except ValueError:
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lbl = self.get_label(node)
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if name == "f":
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return lbl
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elif name == "v":
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return "%sv" % lbl
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elif name == "r":
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return "%sr" % lbl
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elif name == "super":
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return self.get_label(depgraph.nodes[node["head"]])
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elif name == "var":
|
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return "%s%s" % (lbl.upper(), unique_index)
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elif name == "a":
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return self.get_label(self.lookup_unique("conja", node, depgraph))
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elif name == "b":
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return self.get_label(self.lookup_unique("conjb", node, depgraph))
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else:
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return self.get_label(self.lookup_unique(name, node, depgraph))
|
|
|
|
def get_label(self, node):
|
|
"""
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|
Pick an alphabetic character as identifier for an entity in the model.
|
|
|
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:param value: where to index into the list of characters
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:type value: int
|
|
"""
|
|
value = node["address"]
|
|
|
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letter = [
|
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"f",
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"g",
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"h",
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"i",
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"j",
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"k",
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"l",
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"m",
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"n",
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"o",
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"p",
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"q",
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"r",
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"s",
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"t",
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"u",
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"v",
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"w",
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"x",
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"y",
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"z",
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"a",
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"b",
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"c",
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"d",
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"e",
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][value - 1]
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num = int(value) // 26
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if num > 0:
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return letter + str(num)
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else:
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return letter
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|
|
def lookup_unique(self, rel, node, depgraph):
|
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"""
|
|
Lookup 'key'. There should be exactly one item in the associated relation.
|
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"""
|
|
deps = [
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depgraph.nodes[dep]
|
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for dep in chain(*node["deps"].values())
|
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if depgraph.nodes[dep]["rel"].lower() == rel.lower()
|
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]
|
|
|
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if len(deps) == 0:
|
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raise KeyError("'%s' doesn't contain a feature '%s'" % (node["word"], rel))
|
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elif len(deps) > 1:
|
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raise KeyError(
|
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"'%s' should only have one feature '%s'" % (node["word"], rel)
|
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)
|
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else:
|
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return deps[0]
|
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|
|
def get_GlueFormula_factory(self):
|
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return GlueFormula
|
|
|
|
|
|
class Glue(object):
|
|
def __init__(
|
|
self, semtype_file=None, remove_duplicates=False, depparser=None, verbose=False
|
|
):
|
|
self.verbose = verbose
|
|
self.remove_duplicates = remove_duplicates
|
|
self.depparser = depparser
|
|
|
|
from nltk import Prover9
|
|
|
|
self.prover = Prover9()
|
|
|
|
if semtype_file:
|
|
self.semtype_file = semtype_file
|
|
else:
|
|
self.semtype_file = os.path.join(
|
|
"grammars", "sample_grammars", "glue.semtype"
|
|
)
|
|
|
|
def train_depparser(self, depgraphs=None):
|
|
if depgraphs:
|
|
self.depparser.train(depgraphs)
|
|
else:
|
|
self.depparser.train_from_file(
|
|
nltk.data.find(
|
|
os.path.join("grammars", "sample_grammars", "glue_train.conll")
|
|
)
|
|
)
|
|
|
|
def parse_to_meaning(self, sentence):
|
|
readings = []
|
|
for agenda in self.parse_to_compiled(sentence):
|
|
readings.extend(self.get_readings(agenda))
|
|
return readings
|
|
|
|
def get_readings(self, agenda):
|
|
readings = []
|
|
agenda_length = len(agenda)
|
|
atomics = dict()
|
|
nonatomics = dict()
|
|
while agenda: # is not empty
|
|
cur = agenda.pop()
|
|
glue_simp = cur.glue.simplify()
|
|
if isinstance(
|
|
glue_simp, linearlogic.ImpExpression
|
|
): # if cur.glue is non-atomic
|
|
for key in atomics:
|
|
try:
|
|
if isinstance(cur.glue, linearlogic.ApplicationExpression):
|
|
bindings = cur.glue.bindings
|
|
else:
|
|
bindings = linearlogic.BindingDict()
|
|
glue_simp.antecedent.unify(key, bindings)
|
|
for atomic in atomics[key]:
|
|
if not (
|
|
cur.indices & atomic.indices
|
|
): # if the sets of indices are disjoint
|
|
try:
|
|
agenda.append(cur.applyto(atomic))
|
|
except linearlogic.LinearLogicApplicationException:
|
|
pass
|
|
except linearlogic.UnificationException:
|
|
pass
|
|
try:
|
|
nonatomics[glue_simp.antecedent].append(cur)
|
|
except KeyError:
|
|
nonatomics[glue_simp.antecedent] = [cur]
|
|
|
|
else: # else cur.glue is atomic
|
|
for key in nonatomics:
|
|
for nonatomic in nonatomics[key]:
|
|
try:
|
|
if isinstance(
|
|
nonatomic.glue, linearlogic.ApplicationExpression
|
|
):
|
|
bindings = nonatomic.glue.bindings
|
|
else:
|
|
bindings = linearlogic.BindingDict()
|
|
glue_simp.unify(key, bindings)
|
|
if not (
|
|
cur.indices & nonatomic.indices
|
|
): # if the sets of indices are disjoint
|
|
try:
|
|
agenda.append(nonatomic.applyto(cur))
|
|
except linearlogic.LinearLogicApplicationException:
|
|
pass
|
|
except linearlogic.UnificationException:
|
|
pass
|
|
try:
|
|
atomics[glue_simp].append(cur)
|
|
except KeyError:
|
|
atomics[glue_simp] = [cur]
|
|
|
|
for entry in atomics:
|
|
for gf in atomics[entry]:
|
|
if len(gf.indices) == agenda_length:
|
|
self._add_to_reading_list(gf, readings)
|
|
for entry in nonatomics:
|
|
for gf in nonatomics[entry]:
|
|
if len(gf.indices) == agenda_length:
|
|
self._add_to_reading_list(gf, readings)
|
|
return readings
|
|
|
|
def _add_to_reading_list(self, glueformula, reading_list):
|
|
add_reading = True
|
|
if self.remove_duplicates:
|
|
for reading in reading_list:
|
|
try:
|
|
if reading.equiv(glueformula.meaning, self.prover):
|
|
add_reading = False
|
|
break
|
|
except Exception as e:
|
|
# if there is an exception, the syntax of the formula
|
|
# may not be understandable by the prover, so don't
|
|
# throw out the reading.
|
|
print("Error when checking logical equality of statements", e)
|
|
|
|
if add_reading:
|
|
reading_list.append(glueformula.meaning)
|
|
|
|
def parse_to_compiled(self, sentence):
|
|
gfls = [self.depgraph_to_glue(dg) for dg in self.dep_parse(sentence)]
|
|
return [self.gfl_to_compiled(gfl) for gfl in gfls]
|
|
|
|
def dep_parse(self, sentence):
|
|
"""
|
|
Return a dependency graph for the sentence.
|
|
|
|
:param sentence: the sentence to be parsed
|
|
:type sentence: list(str)
|
|
:rtype: DependencyGraph
|
|
"""
|
|
|
|
# Lazy-initialize the depparser
|
|
if self.depparser is None:
|
|
from nltk.parse import MaltParser
|
|
|
|
self.depparser = MaltParser(tagger=self.get_pos_tagger())
|
|
if not self.depparser._trained:
|
|
self.train_depparser()
|
|
return self.depparser.parse(sentence, verbose=self.verbose)
|
|
|
|
def depgraph_to_glue(self, depgraph):
|
|
return self.get_glue_dict().to_glueformula_list(depgraph)
|
|
|
|
def get_glue_dict(self):
|
|
return GlueDict(self.semtype_file)
|
|
|
|
def gfl_to_compiled(self, gfl):
|
|
index_counter = Counter()
|
|
return_list = []
|
|
for gf in gfl:
|
|
return_list.extend(gf.compile(index_counter))
|
|
|
|
if self.verbose:
|
|
print("Compiled Glue Premises:")
|
|
for cgf in return_list:
|
|
print(cgf)
|
|
|
|
return return_list
|
|
|
|
def get_pos_tagger(self):
|
|
from nltk.corpus import brown
|
|
|
|
regexp_tagger = RegexpTagger(
|
|
[
|
|
(r"^-?[0-9]+(.[0-9]+)?$", "CD"), # cardinal numbers
|
|
(r"(The|the|A|a|An|an)$", "AT"), # articles
|
|
(r".*able$", "JJ"), # adjectives
|
|
(r".*ness$", "NN"), # nouns formed from adjectives
|
|
(r".*ly$", "RB"), # adverbs
|
|
(r".*s$", "NNS"), # plural nouns
|
|
(r".*ing$", "VBG"), # gerunds
|
|
(r".*ed$", "VBD"), # past tense verbs
|
|
(r".*", "NN"), # nouns (default)
|
|
]
|
|
)
|
|
brown_train = brown.tagged_sents(categories="news")
|
|
unigram_tagger = UnigramTagger(brown_train, backoff=regexp_tagger)
|
|
bigram_tagger = BigramTagger(brown_train, backoff=unigram_tagger)
|
|
trigram_tagger = TrigramTagger(brown_train, backoff=bigram_tagger)
|
|
|
|
# Override particular words
|
|
main_tagger = RegexpTagger(
|
|
[(r"(A|a|An|an)$", "ex_quant"), (r"(Every|every|All|all)$", "univ_quant")],
|
|
backoff=trigram_tagger,
|
|
)
|
|
|
|
return main_tagger
|
|
|
|
|
|
class DrtGlueFormula(GlueFormula):
|
|
def __init__(self, meaning, glue, indices=None):
|
|
if not indices:
|
|
indices = set()
|
|
|
|
if isinstance(meaning, string_types):
|
|
self.meaning = drt.DrtExpression.fromstring(meaning)
|
|
elif isinstance(meaning, drt.DrtExpression):
|
|
self.meaning = meaning
|
|
else:
|
|
raise RuntimeError(
|
|
"Meaning term neither string or expression: %s, %s"
|
|
% (meaning, meaning.__class__)
|
|
)
|
|
|
|
if isinstance(glue, string_types):
|
|
self.glue = linearlogic.LinearLogicParser().parse(glue)
|
|
elif isinstance(glue, linearlogic.Expression):
|
|
self.glue = glue
|
|
else:
|
|
raise RuntimeError(
|
|
"Glue term neither string or expression: %s, %s"
|
|
% (glue, glue.__class__)
|
|
)
|
|
|
|
self.indices = indices
|
|
|
|
def make_VariableExpression(self, name):
|
|
return drt.DrtVariableExpression(name)
|
|
|
|
def make_LambdaExpression(self, variable, term):
|
|
return drt.DrtLambdaExpression(variable, term)
|
|
|
|
|
|
class DrtGlueDict(GlueDict):
|
|
def get_GlueFormula_factory(self):
|
|
return DrtGlueFormula
|
|
|
|
|
|
class DrtGlue(Glue):
|
|
def __init__(
|
|
self, semtype_file=None, remove_duplicates=False, depparser=None, verbose=False
|
|
):
|
|
if not semtype_file:
|
|
semtype_file = os.path.join(
|
|
"grammars", "sample_grammars", "drt_glue.semtype"
|
|
)
|
|
Glue.__init__(self, semtype_file, remove_duplicates, depparser, verbose)
|
|
|
|
def get_glue_dict(self):
|
|
return DrtGlueDict(self.semtype_file)
|
|
|
|
|
|
def demo(show_example=-1):
|
|
from nltk.parse import MaltParser
|
|
|
|
examples = [
|
|
"David sees Mary",
|
|
"David eats a sandwich",
|
|
"every man chases a dog",
|
|
"every man believes a dog sleeps",
|
|
"John gives David a sandwich",
|
|
"John chases himself",
|
|
]
|
|
# 'John persuades David to order a pizza',
|
|
# 'John tries to go',
|
|
# 'John tries to find a unicorn',
|
|
# 'John seems to vanish',
|
|
# 'a unicorn seems to approach',
|
|
# 'every big cat leaves',
|
|
# 'every gray cat leaves',
|
|
# 'every big gray cat leaves',
|
|
# 'a former senator leaves',
|
|
|
|
print("============== DEMO ==============")
|
|
|
|
tagger = RegexpTagger(
|
|
[
|
|
("^(David|Mary|John)$", "NNP"),
|
|
(
|
|
"^(sees|eats|chases|believes|gives|sleeps|chases|persuades|tries|seems|leaves)$",
|
|
"VB",
|
|
),
|
|
("^(go|order|vanish|find|approach)$", "VB"),
|
|
("^(a)$", "ex_quant"),
|
|
("^(every)$", "univ_quant"),
|
|
("^(sandwich|man|dog|pizza|unicorn|cat|senator)$", "NN"),
|
|
("^(big|gray|former)$", "JJ"),
|
|
("^(him|himself)$", "PRP"),
|
|
]
|
|
)
|
|
|
|
depparser = MaltParser(tagger=tagger)
|
|
glue = Glue(depparser=depparser, verbose=False)
|
|
|
|
for (i, sentence) in enumerate(examples):
|
|
if i == show_example or show_example == -1:
|
|
print("[[[Example %s]]] %s" % (i, sentence))
|
|
for reading in glue.parse_to_meaning(sentence.split()):
|
|
print(reading.simplify())
|
|
print("")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
demo()
|