diff --git a/loader.py b/loader.py
index a7368685..bbf9829e 100644
--- a/loader.py
+++ b/loader.py
@@ -3,6 +3,7 @@
 import codecs
 from utils import create_dico, create_mapping, zero_digits
 from utils import iob2, iob_iobes
+import numpy as np
 
 
 def load_sentences(path, lower, zeros):
@@ -26,6 +27,7 @@ def load_sentences(path, lower, zeros):
     if len(sentence) > 0:
         if 'DOCSTART' not in sentence[0][0]:
             sentences.append(sentence)
+
     return sentences
 
 
@@ -188,3 +190,46 @@ def augment_with_pretrained(dictionary, ext_emb_path, words):
 
     word_to_id, id_to_word = create_mapping(dictionary)
     return dictionary, word_to_id, id_to_word
+
+def load_gazetteers(gaz_path):
+    """
+    Load extra gazetteers.
+    each line consists of two fields
+    gazetteer<tab><category>
+    we could have same gazetteer with multiple categories, but they should appear in
+    different lines
+    """
+    lines = [line.rstrip() for line in open(gaz_path, 'r')]
+    gazetteers = {}
+    tags = {}
+    for line in lines:
+        split = line.split("\t")
+        assert len(split) >= 2
+        category, gazetteer = " ".join(split[-1:]), " ".join(split[:-1])
+        if category not in tags:
+            tags[category] = 1
+
+        if gazetteer not in gazetteers:
+            gazetteers[gazetteer] = []
+            gazetteers[gazetteer].append(category)
+        else:
+            cat = gazetteers[gazetteer]
+            if category not in cat:
+                gazetteers[gazetteer].append(category)
+    return gazetteers,tags.keys()
+
+def add_gazetteers(data, token_to_gazetteers, index_to_token,gaz_tags):
+    """
+    Add gazetteers features to data.
+    """
+    #tags = ['LOC', 'MISC', 'ORG', 'PER']
+    tags = gaz_tags
+    for sentence in data:
+        sentence['gazetteers'] = [np.zeros(len(tags), dtype=np.int32) for _ in xrange(len(sentence['words']))]
+        for i, token_id in enumerate(sentence['words']):
+            token = index_to_token[token_id].lower()
+            if token in token_to_gazetteers:
+                values = set(token_to_gazetteers[token])
+            for j,tag in enumerate(tags):
+                if tag in values:
+                    np.put(sentence['gazetteers'][i],[j],[1])
diff --git a/model.py b/model.py
index fca1ab48..8e7fba56 100644
--- a/model.py
+++ b/model.py
@@ -119,6 +119,7 @@ def build(self,
               lr_method,
               pre_emb,
               crf,
+              gaz_dim,
               cap_dim,
               training=True,
               **kwargs
@@ -135,7 +136,12 @@ def build(self,
         if cap_dim:
             n_cap = 4
 
-        # Network variables
+        #Gaz features
+        if gaz_path:
+            n_gaz = self.parameters['gaz_dim'] # ner tags
+            gaz_dim = n_gaz
+
+        #Network variables
         is_train = T.iscalar('is_train')
         word_ids = T.ivector(name='word_ids')
         char_for_ids = T.imatrix(name='char_for_ids')
@@ -144,7 +150,8 @@ def build(self,
         tag_ids = T.ivector(name='tag_ids')
         if cap_dim:
             cap_ids = T.ivector(name='cap_ids')
-
+        if gaz_path:
+            gaz_values = T.imatrix(name='gaz_values')
         # Sentence length
         s_len = (word_ids if word_dim else char_pos_ids).shape[0]
 
@@ -240,6 +247,12 @@ def build(self,
             cap_layer = EmbeddingLayer(n_cap, cap_dim, name='cap_layer')
             inputs.append(cap_layer.link(cap_ids))
 
+        #gaz features
+        if gaz_path:
+            input_dim += gaz_dim
+            gaz_layer = EmbeddingLayer(n_gaz, gaz_dim, name='gaz_layer')
+            inputs.append(gaz_layer.link(gaz_values))
+
         # Prepare final input
         if len(inputs) != 1:
             inputs = T.concatenate(inputs, axis=1)
@@ -335,6 +348,12 @@ def build(self,
             params.extend(cap_layer.params)
         self.add_component(final_layer)
         params.extend(final_layer.params)
+    # Gazetteers features
+        if gaz_path:
+            self.add_component(gaz_layer)
+            #experiment.components['gaz_embeddings'] = gaz_embeddings
+            params.extend(gaz_layer.params)
+
         if crf:
             self.add_component(transitions)
             params.append(transitions)
@@ -353,6 +372,8 @@ def build(self,
             eval_inputs.append(char_pos_ids)
         if cap_dim:
             eval_inputs.append(cap_ids)
+        if gaz_path:
+            eval_inputs.append(gaz_values)
         train_inputs = eval_inputs + [tag_ids]
 
         # Parse optimization method parameters
diff --git a/train.py b/train.py
index 5dbe0436..cd1242a0 100755
--- a/train.py
+++ b/train.py
@@ -92,6 +92,14 @@
     "-r", "--reload", default="0",
     type='int', help="Reload the last saved model"
 )
+optparser.add_option(
+    "-G", "--gaz_path",default="",
+    help="Gazetteers dimension"
+)
+optparser.add_option(
+    "-g", "--gaz_dim", default="5",
+    help="Gazetteers dimension"
+)
 opts = optparser.parse_args()[0]
 
 # Parse parameters
@@ -111,7 +119,8 @@
 parameters['crf'] = opts.crf == 1
 parameters['dropout'] = opts.dropout
 parameters['lr_method'] = opts.lr_method
-
+parameters['gaz_dim'] = opts.gaz_dim
+parameters['gaz_path'] = opts.gaz_path
 # Check parameters validity
 assert os.path.isfile(opts.train)
 assert os.path.isfile(opts.dev)
@@ -122,7 +131,7 @@
 assert not parameters['all_emb'] or parameters['pre_emb']
 assert not parameters['pre_emb'] or parameters['word_dim'] > 0
 assert not parameters['pre_emb'] or os.path.isfile(parameters['pre_emb'])
-
+assert not parameters['gaz_path'] or os.path.isfile(parameters['gaz_path'])
 # Check evaluation script / folders
 if not os.path.isfile(eval_script):
     raise Exception('CoNLL evaluation script not found at "%s"' % eval_script)
@@ -183,6 +192,17 @@
 print "%i / %i / %i sentences in train / dev / test." % (
     len(train_data), len(dev_data), len(test_data))
 
+if parameters['gaz_dim']:
+    '''1: read from gazetteers file with the format: <gazeetteer <list of categories>>
+       2: once we read the gazetteers, we create a one-hot-encoding gazetteer vector 
+       for every word in the sentence. The length of vector is equal to no of categories
+       and add the gazetteer feature vector for every word
+    '''
+    gazetteers_dataset,gaz_tags = loader.load_gazetteers(parameters['gaz_path'])
+    parameters['gaz_dim'] = len(gaz_tags)
+    loader.add_gazetteers(train_data, gazetteers_dataset, id_to_word,gaz_tags)
+    loader.add_gazetteers(dev_data, gazetteers_dataset, id_to_word,gaz_tags)
+    loader.add_gazetteers(test_data, gazetteers_dataset, id_to_word,gaz_tags)
 # Save the mappings to disk
 print 'Saving the mappings to disk...'
 model.save_mappings(id_to_word, id_to_char, id_to_tag)
diff --git a/utils.py b/utils.py
index 19e3175d..224cf968 100644
--- a/utils.py
+++ b/utils.py
@@ -198,6 +198,8 @@ def create_input(data, parameters, add_label, singletons=None):
     chars = data['chars']
     if singletons is not None:
         words = insert_singletons(words, singletons)
+    if parameters['gaz_path']:
+        gaz = data['gazetteers']
     if parameters['cap_dim']:
         caps = data['caps']
     char_for, char_rev, char_pos = pad_word_chars(chars)
@@ -213,6 +215,8 @@ def create_input(data, parameters, add_label, singletons=None):
         input.append(caps)
     if add_label:
         input.append(data['tags'])
+    if parameters['gaz_path']:
+        input.append(gaz)
     return input