diff --git a/Senseval_Prep/Senseval3/d000.pkl b/Senseval_Prep/Senseval3/d000.pkl
deleted file mode 100644
index 4afe0355e0651408cdd6a096d1a596ceaa9010fe..0000000000000000000000000000000000000000
Binary files a/Senseval_Prep/Senseval3/d000.pkl and /dev/null differ
diff --git a/Senseval_Prep/Senseval3/d001.pkl b/Senseval_Prep/Senseval3/d001.pkl
deleted file mode 100644
index 00ea1ae6369e6f6bd8602a8248a584a482f09c1c..0000000000000000000000000000000000000000
Binary files a/Senseval_Prep/Senseval3/d001.pkl and /dev/null differ
diff --git a/Senseval_Prep/Senseval3/d002.pkl b/Senseval_Prep/Senseval3/d002.pkl
deleted file mode 100644
index 6543b51d28340829cda02e2474349c7916865c93..0000000000000000000000000000000000000000
Binary files a/Senseval_Prep/Senseval3/d002.pkl and /dev/null differ
diff --git a/WSD/embeddings/dummy_gloss_embeddings b/WSD/embeddings/dummy_gloss_embeddings
deleted file mode 100644
index d64c87f9f0ec81e115e37120618346c38120dde1..0000000000000000000000000000000000000000
Binary files a/WSD/embeddings/dummy_gloss_embeddings and /dev/null differ
diff --git a/WSD/embeddings/dummy_gloss_node_emb b/WSD/embeddings/dummy_gloss_node_emb
deleted file mode 100644
index 763b61665e37d30e1e2888b06eec6320a6392375..0000000000000000000000000000000000000000
Binary files a/WSD/embeddings/dummy_gloss_node_emb and /dev/null differ
diff --git a/WSD/embeddings/wsd_dummy_node_emb b/WSD/embeddings/wsd_dummy_node_emb
deleted file mode 100644
index dd0ce650e00042032bd081a75af56327be297c84..0000000000000000000000000000000000000000
Binary files a/WSD/embeddings/wsd_dummy_node_emb and /dev/null differ
diff --git a/WSD/mapping_senseval3/pickle_wn_mapping.py b/WSD/mapping_senseval3/pickle_wn_mapping.py
new file mode 100644
index 0000000000000000000000000000000000000000..b239e6fb1f0148503d30772929647cad6e6e5050
--- /dev/null
+++ b/WSD/mapping_senseval3/pickle_wn_mapping.py
@@ -0,0 +1,19 @@
+import csv
+import pickle
+
+'''
+pickle wordnet mapping as dictionary for use in WSD method 2
+'''
+wordnet_mapping = {}
+with open('wn30_wn17_long-wn17_pos') as csvfile:
+    readCSV = csv.reader(csvfile, delimiter=' ')
+    for row in readCSV:
+        wn3_id = int(row[0])
+        lemma = row[2].split('%')[0]
+        pos = row[3]
+        wn1_7_long = row[2]
+        wordnet_mapping[(wn3_id, lemma, pos)] = wn1_7_long
+
+
+with open("pickled_wn_mapping.pkl", 'wb') as f:
+    pickle.dump(wordnet_mapping, f)
\ No newline at end of file
diff --git a/WSD/mapping_senseval3/pickled_wn_mapping.pkl b/WSD/mapping_senseval3/pickled_wn_mapping.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..9364dc15f879f97e0af6944f3211161cc8a6eb7d
Binary files /dev/null and b/WSD/mapping_senseval3/pickled_wn_mapping.pkl differ
diff --git a/WSD/method2_experiments/mfs_output_context_10 b/WSD/method2_experiments/mfs_output_context_10
new file mode 100644
index 0000000000000000000000000000000000000000..c8d2ee3e6864c0bb7bbbb7a7b9448f099f001350
--- /dev/null
+++ b/WSD/method2_experiments/mfs_output_context_10
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diff --git a/WSD/method2_experiments/mfs_output_context_15 b/WSD/method2_experiments/mfs_output_context_15
new file mode 100644
index 0000000000000000000000000000000000000000..690ae1624c8509dfafd5c1068912ff52fd7a9a99
--- /dev/null
+++ b/WSD/method2_experiments/mfs_output_context_15
@@ -0,0 +1,2081 @@
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diff --git a/WSD/method2_experiments/mfs_output_context_20 b/WSD/method2_experiments/mfs_output_context_20
new file mode 100644
index 0000000000000000000000000000000000000000..675141519c88236a3e2cf2d3fa24fde7ed15d1ff
--- /dev/null
+++ b/WSD/method2_experiments/mfs_output_context_20
@@ -0,0 +1,2081 @@
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diff --git a/WSD/method2_experiments/mfs_output_context_5 b/WSD/method2_experiments/mfs_output_context_5
new file mode 100644
index 0000000000000000000000000000000000000000..f492aec6a301b9798295fa245846696b1b39e840
--- /dev/null
+++ b/WSD/method2_experiments/mfs_output_context_5
@@ -0,0 +1,2081 @@
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+d002 d002.s140.t009 U
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+d002 d002.s140.t014 U
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+d002 d002.s140.t030 smile%1:10:00::
+d002 d002.s140.t032 say%2:32:02::
+d002 d002.s140.t035 U
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+d002 d002.s140.t043 be%2:42:01::
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diff --git a/WSD/mfs_output.txt b/WSD/mfs_output.txt
new file mode 100644
index 0000000000000000000000000000000000000000..efceb492025424649ba2d73c43270cc9d62897b4
--- /dev/null
+++ b/WSD/mfs_output.txt
@@ -0,0 +1,2081 @@
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+d002 d002.s111.t004 stop%1:11:00::
+d002 d002.s111.t006 breathe%2:29:00::
+d002 d002.s111.t009 moment%1:28:00::
+d002 d002.s111.t015 keep%1:21:00::
+d002 d002.s111.t018 U
+d002 d002.s111.t021 lunge%1:04:00::
+d002 d002.s111.t024 doorway%1:06:00::
+d002 d002.s112.t001 U
+d002 d002.s112.t004 say%1:26:00::
+d002 d002.s112.t007 be%1:27:00::
+d002 d002.s112.t009 U
+d002 d002.s112.t010 first%1:24:00::
+d002 d002.s112.t011 session%1:10:00::
+d002 d002.s115.t001 escape%1:04:00::
+d002 d002.s115.t004 U
+d002 d002.s116.t001 trouble%1:09:00::
+d002 d002.s116.t002 be%1:27:00::
+d002 d002.s116.t003 scar%1:26:00::
+d002 d002.s116.t004 family%1:14:02::
+d002 d002.s116.t009 get_through%2:30:00::
+d002 d002.s116.t011 phone_line%1:06:00::
+d002 d002.s116.t016 spend%2:42:00::
+d002 d002.s116.t019 U
+d002 d002.s116.t020 hour%1:28:00::
+d002 d002.s116.t023 U
+d002 d002.s119.t003 be%1:27:00::
+d002 d002.s119.t006 U
+d002 d002.s119.t010 lawn%1:15:00::
+d002 d002.s119.t011 start%1:11:00::
+d002 d002.s119.t013 roll%1:11:02::
+d002 d002.s119.t015 ocean%1:17:00::
+d002 d002.s119.t016 wave%1:11:01::
+d002 d002.s120.t001 run%1:04:01::
+d002 d002.s120.t004 house%1:06:00::
+d002 d002.s120.t007 U
+d002 d002.s120.t012 U
+d002 d002.s120.t013 tremor%1:04:00::
+d002 d002.s120.t014 throw%1:04:00::
+d002 d002.s120.t018 air%1:27:00::
+d002 d002.s120.t020 bounce%1:07:00::
+d002 d002.s120.t024 try%1:04:00::
+d002 d002.s120.t027 U
+d002 d002.s120.t030 foot%1:08:01::
+d002 d002.s121.t001 be%1:27:00::
+d002 d002.s121.t003 fine%1:21:00::
+d002 d002.s121.t010 freak%1:18:00::
+d002 d002.s122.t000 kitchen%1:06:00::
+d002 d002.s122.t001 U
+d002 d002.s122.t004 crystal%1:27:00::
+d002 d002.s123.t000 U
+d002 d002.s123.t002 tape%1:06:00::
+d002 d002.s123.t006 room%1:06:00::
+d002 d002.s124.t002 thing%1:26:00::
+d002 d002.s124.t005 house%1:06:00::
+d002 d002.s124.t006 be%1:27:00::
+d002 d002.s124.t009 be%1:27:00::
+d002 d002.s124.t013 be%1:27:00::
+d002 d002.s124.t018 structure%1:06:00::
+d002 d002.s124.t019 be%1:27:00::
+d002 d002.s124.t020 fine%1:21:00::
+d002 d002.s125.t003 stand%1:06:00::
+d002 d002.s125.t006 lawn%1:15:00::
+d002 d002.s125.t011 wait%1:28:00::
+d002 d002.s125.t014 tremor%1:04:00::
+d002 d002.s125.t017 notice%1:10:01::
+d002 d002.s125.t021 earthworm%1:05:00::
+d002 d002.s125.t023 emerge%2:30:00::
+d002 d002.s125.t026 ground%1:17:00::
+d002 d002.s125.t028 slither%2:38:00::
+d002 d002.s125.t031 lawn%1:15:00::
+d002 d002.s128.t002 be%1:27:00::
+d002 d002.s128.t003 U
+d002 d002.s128.t006 second%1:28:00::
+d002 d002.s128.t010 change%1:11:00::
+d002 d002.s128.t012 life%1:26:01::
+d002 d002.s131.t001 guess%1:10:00::
+d002 d002.s131.t006 live%2:42:08::
+d002 d002.s131.t012 wait%1:28:00::
+d002 d002.s131.t018 aftershock%1:11:00::
+d002 d002.s132.t002 be%1:27:00::
+d002 d002.s132.t003 hard%3:00:06::
+d002 d002.s132.t006 accept%2:31:00::
+d002 d002.s132.t009 be%1:27:00::
+d002 d002.s132.t013 take%1:21:00::
+d002 d002.s132.t015 second%1:28:00::
+d002 d002.s133.t001 wonder%1:12:00::
+d002 d002.s133.t005 be%1:27:00::
+d002 d002.s133.t006 able%3:00:00::
+d002 d002.s133.t009 relax%2:29:00::
+d002 d002.s136.t000 flesh%1:08:02::
+d002 d002.s136.t001 go%1:28:00::
+d002 d002.s136.t003 total%1:06:00::
+d002 d002.s136.t006 flight%1:14:01::
+d002 d002.s136.t008 fight%1:04:01::
+d002 d002.s137.t000 nausea%1:26:00::
+d002 d002.s137.t001 seem%2:39:00::
+d002 d002.s137.t003 commonplace%1:10:00::
+d002 d002.s137.t004 symptom%1:26:00::
+d002 d002.s138.t002 quiet%1:26:00::
+d002 d002.s139.t001 walk%1:04:00::
+d002 d002.s139.t010 few%1:23:00::
+d002 d002.s139.t011 minute%1:28:00::
+d002 d002.s139.t015 morning%1:28:00::
+d002 d002.s140.t000 U
+d002 d002.s140.t005 homeless%1:18:00::
+d002 d002.s140.t006 couple%1:14:00::
+d002 d002.s140.t009 bundle%1:14:00::
+d002 d002.s140.t013 blue%1:07:00::
+d002 d002.s140.t014 U
+d002 d002.s140.t017 sit_up%2:29:00::
+d002 d002.s140.t020 say%1:26:00::
+d002 d002.s140.t023 good_morning%1:10:00::
+d002 d002.s140.t029 woman%1:18:00::
+d002 d002.s140.t030 smile%1:10:00::
+d002 d002.s140.t032 say%1:26:00::
+d002 d002.s140.t035 U
+d002 d002.s140.t039 U
+d002 d002.s140.t043 be%1:27:00::
+d002 d002.s140.t044 alive%3:00:01::
+d002 d002.s141.t001 agree%2:32:00::
+d002 d002.s142.t001 be%1:27:00::
+d002 d002.s143.t000 U
diff --git a/WSD/wsd2_exp.py b/WSD/wsd2_exp.py
index 23da911f4ac710f3bfc6e92374b46bf2ce3ff801..9284279e355d648f4af3f0529a6e84d88877ff4d 100644
--- a/WSD/wsd2_exp.py
+++ b/WSD/wsd2_exp.py
@@ -1,7 +1,11 @@
 import pickle
 import wsd_method2 as wsd
 
-document0 = wsd.read_pkl('Senseval3/d001.pkl')
+document0 = wsd.read_pkl('Senseval3/d000.pkl')
+document1 = wsd.read_pkl('Senseval3/d001.pkl')
+document2 = wsd.read_pkl('Senseval3/d002.pkl')
+
+doc_list = [document0, document1, document2]
 
 # get all required mappings
 id_map = wsd.read_pkl('graph_mappings/id_mapping.pkl')
@@ -9,61 +13,23 @@ reverse_id_map = wsd.read_pkl('graph_mappings/reverse_id.pkl')
 gloss_map = wsd.read_pkl('graph_mappings/gloss_mapping.pkl')
 lemma_map = wsd.read_pkl('graph_mappings/lemma_mapping.pkl')
 pos_map = wsd.read_pkl('graph_mappings/pos_mapping.pkl')
+wordnet_map = wsd.read_pkl('mapping_senseval3/pickled_wn_mapping.pkl')
 
 # get required embeddings
-gloss_emb = wsd.read_pkl('embeddings/dummy_gloss_embeddings')  # dummy gloss embeddings
+gloss_emb = wsd.read_pkl('embeddings/embedding_matrix_gloss')  # dummy gloss embeddings
 lemma_emb = wsd.read_pkl('embeddings/embedding_matrix_lemma')
 pos_emb = wsd.read_pkl('embeddings/embedding_matrix_pos')
-node_emb = wsd.read_pkl('embeddings/wsd_dummy_node_emb')
+node_emb = wsd.read_pkl('embeddings/wsd_node_embeddings.pkl')
 
 disambiguator = wsd.WSD(id_map, reverse_id_map, gloss_map, gloss_emb, lemma_map,
-						lemma_emb, pos_map, pos_emb, node_emb)
-
-window = 5
-
-dummy_head = ['dog', 'NOUN', 'n']
-dummy_context = [['vaporize', 'VERB', 'v'], ['erosion', 'NOUN', 'n'], ['rest', 'VERB', 'v'],
-				 ['resident', 'NOUN', 'n'], ['cut', 'VERB', 'v']]
+						lemma_emb, pos_map, pos_emb, node_emb, wordnet_map)
 
-output_file = "wsd2_output.txt"
 
+# dummy_head = ['dog', 'NOUN', 'n']
+# dummy_context = [['vaporize', 'VERB', 'v'], ['erosion', 'NOUN', 'n'], ['rest', 'VERB', 'v'],
+# 				 ['resident', 'NOUN', 'n'], ['cut', 'VERB', 'v']]
 
-# for i in range(len(document0[0])):
-# 	doc = document0[0]
-# 	check_doc = document0[1]
-# 	token = doc[i]
-# 	check = check_doc[i]
-#
-#
-# 	if check != None:
-#
-# 		if i < window:
-# 			left = doc[:i]
-# 			right = doc[i+1:i + 1 + window]
-# 			context = left + right
-# 			prediction = disambiguator.disambiguate(dummy_context, token, mfs=True)
-# 			print(type(str(prediction)))
-# 			head_id = check[1]['id'][0]
-# 			with open(output_file, 'a') as out:
-# 				try:
-# 					pred_string = str(prediction).split('\'')[1]
-# 				except:
-# 					pred_string = 'U'
-# 				out.write(head_id+'\t'+pred_string+'\n')
-#
-# 		else:
-# 			left = doc[i - window: i]
-# 			right = doc[i + 1:i + 1 + window]
-# 			context = left + right
-# 			prediction = disambiguator.disambiguate(dummy_context, token, mfs=True)
-# 			print(prediction)
-# 			head_id = check[1]['id'][0]
-# 			with open(output_file, 'a') as out:
-# 				try:
-# 					pred_string = str(prediction).split('\'')[1]
-# 				except:
-# 					pred_string = 'U'
-# 				out.write(head_id + '\t' + pred_string + '\n')
+output_file = "method2_experiments/mfs_output_context_"
 
 
 def run_experiment(window_size, document, output_file, mfs=False):
@@ -79,29 +45,37 @@ def run_experiment(window_size, document, output_file, mfs=False):
 				left = doc[:i]
 				right = doc[i + 1:i + 1 + window_size]
 				context = left + right
-				prediction = disambiguator.disambiguate(dummy_context, token, mfs=mfs)
-				print(type(str(prediction)))
+				prediction = disambiguator.disambiguate(context, token, mfs=mfs)
 				head_id = check[1]['id'][0]
-				with open(output_file, 'a') as out:
-					try:
-						pred_string = str(prediction).split('\'')[1]
-					except:
+				doc_id = head_id.split('.')[0]
+				with open(output_file + str(window_size), 'a') as out:
+					if prediction != None:
+						pred_string = prediction
+					else:
 						pred_string = 'U'
-					out.write(head_id + '\t' + pred_string + '\n')
+					out.write(doc_id + ' ' + head_id + ' ' + pred_string + '\n')
 
 			else:
 				left = doc[i - window_size: i]
 				right = doc[i + 1:i + 1 + window_size]
 				context = left + right
-				prediction = disambiguator.disambiguate(dummy_context, token, mfs=mfs)
-				print(prediction)
+				prediction = disambiguator.disambiguate(context, token, mfs=mfs)
+				#print(prediction)
 				head_id = check[1]['id'][0]
-				with open(output_file, 'a') as out:
-					try:
-						pred_string = str(prediction).split('\'')[1]
-					except:
+				doc_id =  head_id.split('.')[0]
+				with open(output_file + str(window_size), 'a') as out:
+					if prediction != None:
+						pred_string = prediction
+					else:
 						pred_string = 'U'
-					out.write(head_id + '\t' + pred_string + '\n')
 
+					# try:
+					# 	pred_string = str(prediction).split('\'')[1]
+					# except:
+					# 	pred_string = 'U'
+					out.write(doc_id + ' ' + head_id + ' ' + pred_string + '\n')
 
-run_experiment(5, document0, output_file, mfs=True)
\ No newline at end of file
+windows = [5,10,15,20]
+for win in windows:
+	for i in doc_list:
+		run_experiment(win, i, output_file)
\ No newline at end of file
diff --git a/WSD/wsd_method2.py b/WSD/wsd_method2.py
index c04bf83ba06f108439e68dea96dfbc5747653be0..5c52949d5fcb5e1482a90f757467def046443cc6 100644
--- a/WSD/wsd_method2.py
+++ b/WSD/wsd_method2.py
@@ -15,7 +15,7 @@ class WSD:
 	"""
 
 	def __init__(self, id_mapping, reverse_id_mapping, gloss_mapping, gloss_embedding, lemma_mapping,
-				lemma_embedding, pos_mapping, pos_embedding, node_embeddings):
+				lemma_embedding, pos_mapping, pos_embedding, node_embeddings, wordnet_mapping):
 
 		self.id_mapping = id_mapping
 		self.reverse_id_mapping = reverse_id_mapping
@@ -26,6 +26,7 @@ class WSD:
 		self.pos_mapping = pos_mapping
 		self.pos_embedding = pos_embedding
 		self.node_embeddings = node_embeddings
+		self.wordnet_mapping = wordnet_mapping
 
 	def disambiguate(self, context, head, mfs=False):
 
@@ -34,12 +35,22 @@ class WSD:
 		Returns most frequent sense if mfs=True.
 		"""
 		token = 0
-		tb_tag = 1
+		tb_tag = 3
 		wn_tag = 2
 
 		if mfs:
+			'''
+			return most frequent sense if there is a synset for the token, else return None'''
 			try:
 				prediction = wn.synsets(head[token])[0]
+			except:
+				return None
+			pred_pos = prediction.pos()
+			pred_id = prediction.offset()
+			pred_lemma = head[token]
+			wn_mapping_key = (pred_id, pred_lemma, pred_pos)
+			try:
+				prediction = self.wordnet_mapping[wn_mapping_key]
 				return prediction
 			except:
 				return None
@@ -47,8 +58,6 @@ class WSD:
 		# get possible synset ids from nltk and map them to EP-graph ids
 
 		head_synsets = wn.synsets(head[token])
-		# if len(head_synsets) < 1:
-		# 	print(head)
 		nltk_synset_keys = [(synset.offset(), synset.pos()) if synset.pos() != 's' else (synset.offset(), 'a') for synset in head_synsets]
 		EP_synset_ids = []
 		tuples_not_mapped = 0
@@ -82,10 +91,9 @@ class WSD:
 				gloss.append(self.gloss_mapping[(word[token], word[tb_tag])])
 			except KeyError:
 				oov.append(word)
-		print(oov)
 		gloss_length = len(gloss)
 		gloss = np.array(gloss)
-		print(gloss)
+		#print(gloss)
 		gloss_emb = self.gloss_embedding[gloss]
 		gloss_repr = np.add.reduce(gloss_emb) / gloss_length
 
@@ -103,11 +111,21 @@ class WSD:
 		# return synset_id with nearest
 		nearest_sense = EP_synset_ids[distances.index(min(distances))]
 		wn_nearest = self.reverse_id_mapping[nearest_sense][::-1]
-		# print(wn_nearest)
+		pred_pos = wn_nearest[0]
+		pred_id = wn_nearest[1]
+		pred_lemma = head[token]
+		wn_mapping_key = (pred_id, pred_lemma, pred_pos)
+		try:
+			prediction = self.wordnet_mapping[wn_mapping_key]
+		except:
+			return None
+		#print(pred_id, pred_lemma, pred_pos)
 		nearest_sense = wn.synset_from_pos_and_offset(*wn_nearest)
 
-		return nearest_sense #, tuples_not_mapped  maybe also oov
+		return prediction
+		#return pred_id, pred_lemma, pred_pos #, tuples_not_mapped  maybe also oov
 
+#TODO: return error codes instead of None, so the cause and amount can be determined in each experiment
 
 if __name__ == "__main__":
 	# get all required mappings