clean up import statements

nltk/chunk/__init__.py

@@ -152,11 +152,12 @@ zero-length assertions).
      pattern is valid.
 """
 
-from api import ChunkParserI
-from util import (ChunkScore, accuracy, tagstr2tree, conllstr2tree,
+from nltk.data import load
+
+from nltk.chunk.api import ChunkParserI
+from nltk.chunk.util import (ChunkScore, accuracy, tagstr2tree, conllstr2tree,
                             tree2conlltags, tree2conllstr, tree2conlltags)
-from regexp import RegexpChunkParser, RegexpParser
-import nltk
+from nltk.chunk.regexp import RegexpChunkParser, RegexpParser
 
 # Standard treebank POS tagger
 _BINARY_NE_CHUNKER = 'chunkers/maxent_ne_chunker/english_ace_binary.pickle'
@@ -171,7 +172,7 @@ def ne_chunk(tagged_tokens, binary=False):
         chunker_pickle = _BINARY_NE_CHUNKER
     else:
         chunker_pickle = _MULTICLASS_NE_CHUNKER
-    chunker = nltk.data.load(chunker_pickle)
+    chunker = load(chunker_pickle)
     return chunker.parse(tagged_tokens)
 
 def batch_ne_chunk(tagged_sentences, binary=False):
@@ -183,6 +184,6 @@ def batch_ne_chunk(tagged_sentences, binary=False):
         chunker_pickle = _BINARY_NE_CHUNKER
     else:
         chunker_pickle = _MULTICLASS_NE_CHUNKER
-    chunker = nltk.data.load(chunker_pickle)
+    chunker = load(chunker_pickle)
     return chunker.batch_parse(tagged_sentences)
 

nltk/chunk/api.py

@@ -11,7 +11,8 @@
 ##//////////////////////////////////////////////////////
 
 from nltk.parse import ParserI
-import nltk
+
+from nltk.chunk.util import ChunkScore
 
 class ChunkParserI(ParserI):
     """
@@ -43,7 +44,7 @@ class ChunkParserI(ParserI):
         :param gold: The list of chunked sentences to score the chunker on.
         :rtype:  L{ChunkScore<nltk.chunk.util.ChunkScore>}
         """
-        chunkscore = nltk.chunk.util.ChunkScore()
+        chunkscore = ChunkScore()
         for correct in gold:
             chunkscore.score(correct, self.parse(correct.leaves()))
         return chunkscore

nltk/cluster/__init__.py

@@ -79,8 +79,8 @@ objects. nltk_contrib.unimelb.tacohn.SparseArrays may be used for
 efficiency when required.
 """
 
-from util import (VectorSpaceClusterer, Dendrogram,
+from nltk.cluster.util import (VectorSpaceClusterer, Dendrogram,
                                euclidean_distance, cosine_distance)
-from kmeans import KMeansClusterer
-from gaac import GAAClusterer
-from em import EMClusterer
+from nltk.cluster.kmeans import KMeansClusterer
+from nltk.cluster.gaac import GAAClusterer
+from nltk.cluster.em import EMClusterer