Given a word, guess the appropriate country by word vector.

        Parameters
        ---------
        text: str
            the text to extract locations from.

        Returns
        -------
        country_picking: dict
            The top two countries (ISO codes) and two measures
            confidence for the first choice.

def _feature_word_embedding(self, text):
        """
        Given a word, guess the appropriate country by word vector.

        Parameters
        ---------
        text: str
            the text to extract locations from.

        Returns
        -------
        country_picking: dict
            The top two countries (ISO codes) and two measures
            confidence for the first choice.
        """
        try:
            simils = np.dot(self._prebuilt_vec, text.vector)
        except Exception as e:
            #print("Vector problem, ", Exception, e)
            return {"country_1" : "",
                "confid_a" : 0,
                "confid_b" : 0,
                "country_2" : ""}
        ranks = simils.argsort()[::-1]
        confid = simils.max()
        confid2 = simils[ranks[0]] - simils[ranks[1]]
        if confid == 0 or confid2 == 0:
            return ""
        country_code = self._cts[str(self._ct_nlp[ranks[0]])]
        country_picking = {"country_1" : country_code,
                "confid_a" : confid,
                "confid_b" : confid2,
                "country_2" : self._cts[str(self._ct_nlp[ranks[1]])]}
        return country_picking