From b005e7af5f671b31b45229d92208572c8d378c38 Mon Sep 17 00:00:00 2001
From: snrore <35712574+snrore@users.noreply.github.com>
Date: Fri, 1 Jan 2021 19:33:13 -0500
Subject: [PATCH] Synset path_similarity commutativity (#2650)

* add test

* need_root

* update

* fix tab

* fix tab

* wup similarity
---
 nltk/corpus/reader/wordnet.py  | 8 +++++---
 nltk/test/unit/test_wordnet.py | 4 ++++
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/nltk/corpus/reader/wordnet.py b/nltk/corpus/reader/wordnet.py
index 7d20e6f10c..1dab0eb729 100644
--- a/nltk/corpus/reader/wordnet.py
+++ b/nltk/corpus/reader/wordnet.py
@@ -845,7 +845,7 @@ def path_similarity(self, other, verbose=False, simulate_root=True):
         """
 
         distance = self.shortest_path_distance(
-            other, simulate_root=simulate_root and self._needs_root()
+            other, simulate_root=simulate_root and (self._needs_root() or other._needs_root()) 
         )
         if distance is None or distance < 0:
             return None
@@ -934,13 +934,15 @@ def wup_similarity(self, other, verbose=False, simulate_root=True):
             the two senses can be found, None is returned.
 
         """
+        need_root = self._needs_root() or other._needs_root()
 
-        need_root = self._needs_root()
         # Note that to preserve behavior from NLTK2 we set use_min_depth=True
         # It is possible that more accurate results could be obtained by
         # removing this setting and it should be tested later on
         subsumers = self.lowest_common_hypernyms(
-            other, simulate_root=simulate_root and need_root, use_min_depth=True
+            other,
+            simulate_root=simulate_root and need_root,
+            use_min_depth=True
         )
 
         # If no LCS was found return None
diff --git a/nltk/test/unit/test_wordnet.py b/nltk/test/unit/test_wordnet.py
index 97d5d4d92f..b58c845b05 100644
--- a/nltk/test/unit/test_wordnet.py
+++ b/nltk/test/unit/test_wordnet.py
@@ -172,12 +172,16 @@ def test_wordnet_similarities(self):
         # Path based similarities.
         self.assertAlmostEqual(S('cat.n.01').path_similarity(S('cat.n.01')), 1.0)
         self.assertAlmostEqual(S('dog.n.01').path_similarity(S('cat.n.01')), 0.2)
+        self.assertAlmostEqual(S('car.n.01').path_similarity(S('automobile.v.01')),
+                               S('automobile.v.01').path_similarity(S('car.n.01')))
         self.assertAlmostEqual(
             S('dog.n.01').lch_similarity(S('cat.n.01')), 2.028, places=3
         )
         self.assertAlmostEqual(
             S('dog.n.01').wup_similarity(S('cat.n.01')), 0.8571, places=3
         )
+        self.assertAlmostEqual(S('car.n.01').wup_similarity(S('automobile.v.01')),
+                               S('automobile.v.01').wup_similarity(S('car.n.01')))
         # Information Content similarities.
         brown_ic = wnic.ic('ic-brown.dat')
         self.assertAlmostEqual(