Semantic Similarity Files

BrownIC.py

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+import nltk
+from nltk.corpus import wordnet as wn
+from nltk.corpus import wordnet_ic
+import math,csv
+import scipy
+
+brown_ic = wordnet_ic.ic('ic-brown.dat')
+
+def sim_lin(syns1,syns2):
+    maxSim=None
+    for s1 in syns1:
+        for s2 in syns2:
+            sim=s1.lin_similarity(s2,brown_ic)
+            if maxSim==None or maxSim<sim:
+                maxSim=sim
+    return maxSim
+
+def sim_resnik(syns1,syns2):
+    maxSim=None
+    for s1 in syns1:
+        for s2 in syns2:
+            sim=s1.res_similarity(s2,brown_ic)
+            if maxSim==None or maxSim<sim:
+                maxSim=sim
+    return maxSim
+
+train = csv.reader(open("rg.csv",'rb'),delimiter=';')
+
+word1=[]
+word2=[]
+hr=[]
+LinS=[]
+
+for row in train:
+    word1.append(row[0])
+    word2.append(row[1])
+    hr.append(row[2])
+
+#f=open("BrownIC.txt","w")
+
+for i in range(1,len(hr)):
+    a=wn.synsets(word1[i],pos="n")
+    b=wn.synsets(word2[i],pos="n")
+    LinS.append(sim_resnik(a,b))
+    #print sim_lin(a,b)
+    #f.write("%s\t%s\t%.5s\t%.5s\t%.5s\n"%(word1[i],word2[i],sim_lin(a,b),sim_resnik(a,b),hr[i]))
+
+#f.close()
+hr.pop(0)
+print scipy.stats.spearmanr(LinS,hr)