# Path: dsenser/constants.py
# ENCODING = "utf-8"
# 
# DFLT_BROWN_PATH = os.path.join(DATA_DIR, "brown_cluster_1000.txt")
# 
# DFLT_ECONN_PATH = os.path.join(ECONN_DIR, "ExpConn.txt")
# 
# DFLT_INQUIRER_PATH = os.path.join(DATA_DIR, "inquirer_word")
# 
# DFLT_LCSI_PATH = os.path.join(DATA_DIR, "LCSInfomerge.txt")
# 
# DFLT_MPQA_PATH = os.path.join(DATA_DIR, "MPQA_Subjectivity_Lexicon.txt")
# 
# DFLT_W2V_PATH = os.path.join(DATA_DIR, "GoogleNews-vectors-negative300.bin")


from dsenser.constants import ENCODING, DFLT_BROWN_PATH, DFLT_ECONN_PATH, \
    DFLT_INQUIRER_PATH, DFLT_LCSI_PATH, DFLT_MPQA_PATH, DFLT_W2V_PATH
from dsenser.scorer.conn_head_mapper import ConnHeadMapper
from collections import defaultdict
from nltk.stem.porter import PorterStemmer
    from gensim.models.word2vec import Word2Vec
import codecs
import gc
import re
import sys
    with codecs.open(a_fname, 'r', ENCODING,
                     errors="replace") as ifile:
        for iline in ifile:
            iline = iline.strip()
            if not iline:
                continue
            iword, iclass_str = SPACE_RE.split(iline, 1)
            iword = iword.lower()
            iclasses = set(HASH_RE.split(iclass_str))
            if iword in ret:
                ret[iword].update(iclasses)
            else:
                ret[iword] = iclasses
    return ret


def load_BROWN(a_fname):
    """Load BROWN clusters from file.

    Args:
      a_fname (str): file containing Brown clusters

    Returns:
      dict:
        mapping from word to a set of Brown clusters

    """
    ret = defaultdict(set)
    iword = iclass = None
    print("Loading {:s}... ".format(a_fname), end="", file=sys.stderr)

# Path: dsenser/constants.py
# ENCODING = "utf-8"
# 
# DFLT_BROWN_PATH = os.path.join(DATA_DIR, "brown_cluster_1000.txt")
# 
# DFLT_ECONN_PATH = os.path.join(ECONN_DIR, "ExpConn.txt")
# 
# DFLT_INQUIRER_PATH = os.path.join(DATA_DIR, "inquirer_word")
# 
# DFLT_LCSI_PATH = os.path.join(DATA_DIR, "LCSInfomerge.txt")
# 
# DFLT_MPQA_PATH = os.path.join(DATA_DIR, "MPQA_Subjectivity_Lexicon.txt")
# 
# DFLT_W2V_PATH = os.path.join(DATA_DIR, "GoogleNews-vectors-negative300.bin")


# Path: dsenser/resources.py
from dsenser.constants import ENCODING, DFLT_BROWN_PATH, DFLT_ECONN_PATH, \
    DFLT_INQUIRER_PATH, DFLT_LCSI_PATH, DFLT_MPQA_PATH, DFLT_W2V_PATH
from dsenser.scorer.conn_head_mapper import ConnHeadMapper
from collections import defaultdict
from nltk.stem.porter import PorterStemmer
    from gensim.models.word2vec import Word2Vec
import codecs
import gc
import re
import sys

    with codecs.open(a_fname, 'r', ENCODING,
                     errors="replace") as ifile:
        for iline in ifile:
            iline = iline.strip()
            if not iline:
                continue
            iword, iclass_str = SPACE_RE.split(iline, 1)
            iword = iword.lower()
            iclasses = set(HASH_RE.split(iclass_str))
            if iword in ret:
                ret[iword].update(iclasses)
            else:
                ret[iword] = iclasses
    return ret


def load_BROWN(a_fname):
    """Load BROWN clusters from file.

    Args:
      a_fname (str): file containing Brown clusters

    Returns:
      dict:
        mapping from word to a set of Brown clusters

    """
    ret = defaultdict(set)
    iword = iclass = None
    print("Loading {:s}... ".format(a_fname), end="", file=sys.stderr)

    with codecs.open(a_fname, 'r', ENCODING,