diff --git a/spacy/en/__init__.py b/spacy/en/__init__.py
index 682c0c658..ca4518a60 100644
--- a/spacy/en/__init__.py
+++ b/spacy/en/__init__.py
@@ -5,9 +5,10 @@ import re
 from .. import orth
 from ..vocab import Vocab
 from ..tokenizer import Tokenizer
-from ..syntax import parser
 from ..syntax.arc_eager import ArcEager
 from ..syntax.ner import BiluoPushDown
+from ..syntax.parser import ParserFactory
+
 from ..tokens import Tokens
 from ..multi_words import RegexMerger
 
@@ -36,10 +37,7 @@ def get_lex_props(string):
         'sentiment': 0
     }
 
-
-LOCAL_DATA_DIR = path.join(path.dirname(__file__), 'data')
-
-parse_if_model_present = -1
+if_model_present = -1
 
 
 class English(object):
@@ -63,45 +61,23 @@ class English(object):
         Start with nothing loaded:
 
             >>> nlp = English(data_dir=None)
-
-    Keyword args:
-        data_dir (unicode):
-            A path to a directory from which to load the pipeline;
-            or '', to load default; or None, to load nothing.
-
-        Tokenizer (bool or callable):
-            desc
-
-        Vectors (bool or callable):
-            desc
-
-        Parser (bool or callable):
-            desc
-
-        Tagger (bool or callable):
-            desc
-
-        Entity (bool or callable):
-            desc
-
-        Senser (bool or callable):
-            desc
     """
     ParserTransitionSystem = ArcEager
     EntityTransitionSystem = BiluoPushDown
 
-    def __init__(self, data_dir='', Tokenizer=Tokenizer.from_dir, Vectors=True,
-                 Parser=True, Tagger=EnPosTagger, Entity=True, load_vectors=True):
-        if data_dir == '':
-            data_dir = LOCAL_DATA_DIR
+    def __init__(self,
+      data_dir=path.join(path.dirname(__file__), 'data'),
+      Tokenizer=Tokenizer.from_dir,
+      Tagger=EnPosTagger,
+      Parser=ParserFactory(ParserTransitionSystem),
+      Entity=ParserFactory(EntityTransitionSystem),
+      load_vectors=True
+    ):
+        
         self._data_dir = data_dir
         
-        # TODO: Deprecation warning
-        if load_vectors is False:
-            vectors = False
-
         self.vocab = Vocab(data_dir=path.join(data_dir, 'vocab') if data_dir else None,
-                           get_lex_props=get_lex_props, load_vectors=Vectors,
+                           get_lex_props=get_lex_props, load_vectors=load_vectors,
                            pos_tags=POS_TAGS)
         if Tagger is True:
             Tagger = EnPosTagger
@@ -112,10 +88,8 @@ class English(object):
             transition_system = self.EntityTransitionSystem
             Entity = lambda s, d: parser.Parser(s, d, transition_system)
 
-        if Tokenizer: 
-            self.tokenizer = Tokenizer(self.vocab, path.join(data_dir, 'tokenizer'))
-        else:
-            self.tokenizer = None
+        self.tokenizer = Tokenizer(self.vocab, path.join(data_dir, 'tokenizer'))
+        
         if Tagger:
             self.tagger = Tagger(self.vocab.strings, data_dir)
         else:
@@ -128,33 +102,20 @@ class English(object):
             self.entity = Entity(self.vocab.strings, path.join(data_dir, 'ner'))
         else:
             self.entity = None
-
         self.mwe_merger = RegexMerger([
             ('IN', 'O', regexes.MW_PREPOSITIONS_RE),
             ('CD', 'TIME', regexes.TIME_RE),
             ('NNP', 'DATE', regexes.DAYS_RE),
             ('CD', 'MONEY', regexes.MONEY_RE)])
 
-    def __call__(self, text, tag=True, parse=parse_if_model_present,
-                 entity=parse_if_model_present, merge_mwes=False):
+    def __call__(self, text, tag=True, parse=True, entity=True):
         """Apply the pipeline to some text.  The text can span multiple sentences,
         and can contain arbtrary whitespace.  Alignment into the original string
-
-        The tagger and parser are lazy-loaded the first time they are required.
-        Loading the parser model usually takes 5-10 seconds.
-
+        is preserved.
+        
         Args:
             text (unicode): The text to be processed.
 
-        Keyword args:
-            tag (bool): Whether to add part-of-speech tags to the text.  Also
-                sets morphological analysis and lemmas.
-
-            parse (True, False, -1): Whether to add labelled syntactic dependencies.
-
-              -1 (default) is "guess": It will guess True if tag=True and the
-                model has been installed.
-
         Returns:
             tokens (spacy.tokens.Tokens):
 
@@ -164,36 +125,13 @@ class English(object):
         >>> tokens[0].orth_, tokens[0].head.tag_
         ('An', 'NN')
         """
-        if parse == True and tag == False:
-            msg = ("Incompatible arguments: tag=False, parse=True"
-                   "Part-of-speech tags are required for parsing.")
-            raise ValueError(msg)
-        if entity == True and tag == False:
-            msg = ("Incompatible arguments: tag=False, entity=True"
-                   "Part-of-speech tags are required for entity recognition.")
-            raise ValueError(msg)
-
         tokens = self.tokenizer(text)
-        if parse == -1 and tag == False:
-            parse = False
-        elif parse == -1 and self.parser is None:
-            parse = False
-        if entity == -1 and tag == False:
-            entity = False
-        elif entity == -1 and self.entity is None:
-            entity = False
-        if tag:
-            ModelNotLoaded.check(self.tagger, 'tagger')
+        if self.tagger and tag:
             self.tagger(tokens)
-        if parse:
-            ModelNotLoaded.check(self.parser, 'parser')
+        if self.parser and parse:
             self.parser(tokens)
-        if entity:
-            ModelNotLoaded.check(self.entity, 'entity')
+        if self.entity and entity:
             self.entity(tokens)
-
-        if merge_mwes and self.mwe_merger is not None:
-            self.mwe_merger(tokens)
         return tokens
 
     @property