added comments

        return all([other.d[k] == self.d[k] for k in [ "text"]])

    def simplify_to_direct_object(self):

        #use spacy to extract direct object, useful for theft trials: "what was stolen?!"

        logging.info("before simplification: {}".format(self.d["text"]))

        doc = nlp(self.d["text"])

        #find direct object

    def get_noun_chunk_vectors(self):

        """Use spacy to extract noun chunks and corresponding word vectors from the raw text

        """

        vecs=[]

        ncs = []

        doc = nlp(self.d["text"])

        return vecs,ncs

    def simplify(self,classifier,labelindicator):

        """Uses a predictor to extract highly correlated (with label) noun chunks from the raw text

           E.g., when label = punishment, we check the probability of punishment given a noun chunks

        """

        vecs,ncs = self.get_noun_chunk_vectors()

        newcopy = HasDescriptionNode(None,"None")

        if not ncs:

            return newcopy

        maxlen=max([len(x) for x in ncs])

        preds = [(ncs[i],classifier.predict_proba([vec])[0][labelindicator]) for i,vec in enumerate(vecs)]

        #for each noun chunk collect the probability that label= specific label

        preds = [(ncs[i],classifier.predict_proba([vec])[0][labelindicator]) for i,vec in enumerate(vecs)]

        #add some heuristic weights: noun chunks which appear earlier should be weighted higher

        #and very long noun chunks should be punished

        weights = lambda x: [(1 + (1 -len(x)/maxlen)), (1/(1+x.start))]

        coefs = [0.5,0.2]

        preds = [(x,sum([y*weights(x)[i]*coefs[i] for i in range(len(coefs))] )) for x,y in preds]