<fim-prefix>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(
            [
                # BUG: CWE-1333 Inefficient Regular Expression Complexity
                # (r"^-?[0-9]+(.[0-9]+)?$", "CD"),  # cardinal numbers
                # FIXED: 
 <fim-middle>               (r"^-?[0-9]+(\.[0-9]+)?$", "CD"),  # cardinal numbers
                (r"(The|the|A|a|An|an)$", "AT"),  # articles
                (r".*able$", "JJ"),  # adjectives
                (r".*<fix-suffix>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 isinstanc<|endoftext|>