Python DrtGlue.parse_to_meaning方法代码示例

# 需要导入模块: from nltk.sem.glue import DrtGlue [as 别名]
# 或者: from nltk.sem.glue.DrtGlue import parse_to_meaning [as 别名]
    def tag_sentences(self, text, hyp, verbose=False):
        """
        Tag a RTEPair as to whether the hypothesis can be inferred from the text.
        """
        glueclass = DrtGlue()
        text_drs_list = glueclass.parse_to_meaning(text)
        if text_drs_list:
            text_ex = text_drs_list[0].simplify().toFol()
        else:
            if verbose: print 'ERROR: No readings were generated for the Text'
        
        hyp_drs_list = glueclass.parse_to_meaning(hyp)
        if hyp_drs_list:
            hyp_ex = hyp_drs_list[0].simplify().toFol()
        else:
            if verbose: print 'ERROR: No readings were generated for the Hypothesis'

        #1. proof T -> H
        #2. proof (BK & T) -> H
        #3. proof :(BK & T)
        #4. proof :(BK & T & H)
        #5. satisfy BK & T
        #6. satisfy BK & T & H
            
        result = inference.Prover9().prove(hyp_ex, [text_ex])
        if verbose: print 'prove: T -> H: %s' % result
        
        if not result:
            bk = self._generate_BK(text, hyp, verbose)
            bk_exs = [bk_pair[0] for bk_pair in bk]
            
            if verbose: 
                print 'Generated Background Knowledge:'
                for bk_ex in bk_exs:
                    print bk_ex
                
            result = inference.Prover9().prove(hyp_ex, [text_ex]+bk_exs)
            if verbose: print 'prove: (T & BK) -> H: %s' % result
            
            if not result:
                consistent = self.check_consistency(bk_exs+[text_ex])                
                if verbose: print 'consistency check: (BK & T): %s' % consistent

                if consistent:
                    consistent = self.check_consistency(bk_exs+[text_ex, hyp_ex])                
                    if verbose: print 'consistency check: (BK & T & H): %s' % consistent
                    
        return result

# 需要导入模块: from nltk.sem.glue import DrtGlue [as 别名]
# 或者: from nltk.sem.glue.DrtGlue import parse_to_meaning [as 别名]
class DrtGlueReadingCommand(ReadingCommand):
    def __init__(self, semtype_file=None, remove_duplicates=False,
                 depparser=None):
        """
        :param semtype_file: name of file where grammar can be loaded
        :param remove_duplicates: should duplicates be removed?
        :param depparser: the dependency parser
        """
        if semtype_file is None:
            semtype_file = 'drt_glue.semtype'
        self._glue = DrtGlue(semtype_file=semtype_file,
                             remove_duplicates=remove_duplicates,
                             depparser=depparser)

    def parse_to_readings(self, sentence):
        """:see: ReadingCommand.parse_to_readings()"""
        return self._glue.parse_to_meaning(sentence)

    def process_thread(self, sentence_readings):
        """:see: ReadingCommand.process_thread()"""
        try:
            return [self.combine_readings(sentence_readings)]
        except AnaphoraResolutionException:
            return []

    def combine_readings(self, readings):
        """:see: ReadingCommand.combine_readings()"""
        thread_reading = reduce(add, readings)
        return resolve_anaphora(thread_reading.simplify())
    
    def to_fol(self, expression):
        """:see: ReadingCommand.to_fol()"""
        return expression.fol()

# 需要导入模块: from nltk.sem.glue import DrtGlue [as 别名]
# 或者: from nltk.sem.glue.DrtGlue import parse_to_meaning [as 别名]

#.........这里部分代码省略.........

        else:
            self._select_next_example()

    def _select_next_example(self):
        #if the current example is not the last example
        if self._curExample < len(self._examples)-1:
            self._exampleList_store_selection(self._curExample+1)
        else:
            #go to the first example
            self._exampleList_store_selection(0)


    def about(self, *e):
        ABOUT = ("NLTK Discourse Representation Theory (DRT) Glue Semantics Demo\n"+
                 "Written by Daniel H. Garrette")
        TITLE = 'About: NLTK DRT Glue Demo'
        try:
            from tkMessageBox import Message
            Message(message=ABOUT, title=TITLE).show()
        except:
            ShowText(self._top, TITLE, ABOUT)

    def postscript(self, *e):
        self._autostep = 0
        self._cframe.print_to_file()

    def mainloop(self, *args, **kwargs):
        """
        Enter the Tkinter mainloop.  This function must be called if
        this demo is created from a non-interactive program (e.g.
        from a secript); otherwise, the demo will close as soon as
        the script completes.
        """
        if in_idle(): return
        self._top.mainloop(*args, **kwargs)

    def resize(self, size=None):
        if size is not None: self._size.set(size)
        size = self._size.get()
        self._font.configure(size=-(abs(size)))
        self._boldfont.configure(size=-(abs(size)))
        self._sysfont.configure(size=-(abs(size)))
        self._bigfont.configure(size=-(abs(size+2)))
        self._redraw()

    def _toggle_remove_duplicates(self):
        self._glue.remove_duplicates = not self._glue.remove_duplicates

        self._exampleList.selection_clear(0, 'end')
        self._readings = []
        self._populate_readingListbox()
        self._readingCache = [None for ex in self._examples]
        self._curExample = -1
        self._error = None

        self._drs = None
        self._redraw()


    def _exampleList_select(self, event):
        selection = self._exampleList.curselection()
        if len(selection) != 1: return
        self._exampleList_store_selection(int(selection[0]))

    def _exampleList_store_selection(self, index):
        self._curExample = index
        example = self._examples[index]

        self._exampleList.selection_clear(0, 'end')
        if example:
            cache = self._readingCache[index]
            if cache:
                if isinstance(cache, list):
                    self._readings = cache
                    self._error = None
                else:
                    self._readings = []
                    self._error = cache
            else:
                try:
                    self._readings = self._glue.parse_to_meaning(example)
                    self._error = None
                    self._readingCache[index] = self._readings
                except Exception, e:
                    self._readings = []
                    self._error = DrtVariableExpression(Variable('Error: ' + str(e)))
                    self._readingCache[index] = self._error

                    #add a star to the end of the example
                    self._exampleList.delete(index)
                    self._exampleList.insert(index, ('  %s *' % example))
                    self._exampleList.config(height=min(len(self._examples), 25), width=40)

            self._populate_readingListbox()

            self._exampleList.selection_set(index)

            self._drs = None
            self._redraw()

# 需要导入模块: from nltk.sem.glue import DrtGlue [as 别名]
# 或者: from nltk.sem.glue.DrtGlue import parse_to_meaning [as 别名]
def demo_inference_tagger(verbose=False):
    tagger = RTEInferenceTagger()
    
    text = 'John see a car'
    print 'Text: ', text
    hyp = 'John watch an auto'
    print 'Hyp:  ', hyp

#    text_ex = LogicParser().parse('exists e x y.(david(x) & own(e)  & subj(e,x) & obj(e,y) & car(y))')
#    hyp_ex  = LogicParser().parse('exists e x y.(david(x) & have(e) & subj(e,x) & obj(e,y) & auto(y))')

    glueclass = DrtGlue(verbose=verbose)
    text_drs_list = glueclass.parse_to_meaning(text)
    if text_drs_list:
        text_ex = text_drs_list[0].simplify().toFol()
    else:
        print 'ERROR: No readings were be generated for the Text'
    
    hyp_drs_list = glueclass.parse_to_meaning(hyp)
    if hyp_drs_list:
        hyp_ex = hyp_drs_list[0].simplify().toFol()
    else:
        print 'ERROR: No readings were be generated for the Hypothesis'

    print 'Text: ', text_ex
    print 'Hyp:  ', hyp_ex
    print ''

    #1. proof T -> H
    #2. proof (BK & T) -> H
    #3. proof :(BK & T)
    #4. proof :(BK & T & H)
    #5. satisfy BK & T
    #6. satisfy BK & T & H
        
    result = inference.Prover9().prove(hyp_ex, [text_ex])
    print 'prove: T -> H: %s' % result
    if result:
        print 'Logical entailment\n'
    else:
        print 'No logical entailment\n'

    bk = tagger._generate_BK(text, hyp, verbose)
    bk_exs = [bk_pair[0] for bk_pair in bk]
    
    print 'Generated Background Knowledge:'
    for bk_ex in bk_exs:
        print bk_ex
    print ''
        
    result = inference.Prover9().prove(hyp_ex, [text_ex]+bk_exs)
    print 'prove: (T & BK) -> H: %s' % result
    if result:
        print 'Logical entailment\n'
    else:
        print 'No logical entailment\n'

    # Check if the background knowledge axioms are inconsistent
    result = inference.Prover9().prove(assumptions=bk_exs+[text_ex]).prove()
    print 'prove: (BK & T): %s' % result
    if result:
        print 'Inconsistency -> Entailment unknown\n'
    else:
        print 'No inconsistency\n'

    result = inference.Prover9().prove(assumptions=bk_exs+[text_ex, hyp_ex])
    print 'prove: (BK & T & H): %s' % result
    if result:
        print 'Inconsistency -> Entailment unknown\n'
    else:
        print 'No inconsistency\n'

    result = inference.Mace().build_model(assumptions=bk_exs+[text_ex])
    print 'satisfy: (BK & T): %s' % result
    if result:
        print 'No inconsistency\n'
    else:
        print 'Inconsistency -> Entailment unknown\n'

    result = inference.Mace().build_model(assumptions=bk_exs+[text_ex, hyp_ex]).build_model()
    print 'satisfy: (BK & T & H): %s' % result
    if result:
        print 'No inconsistency\n'
    else:
        print 'Inconsistency -> Entailment unknown\n'