diff --git a/index.js b/index.js
index 3e84d88..295d043 100644
--- a/index.js
+++ b/index.js
@@ -19,6 +19,12 @@ var visit = require('unist-util-visit');
 var nlcstToString = require('nlcst-to-string');
 var pos = require('retext-pos');
 
+/*
+ * Methods.
+ */
+
+var own = Object.prototype.hasOwnProperty;
+
 /**
  * Get the stem of a node.
  *
@@ -92,7 +98,7 @@ function getImportantWords(node) {
                 'parent': parent
             };
 
-            if (!(stem in words)) {
+            if (!own.call(words, stem)) {
                 words[stem] = {
                     'matches': [match],
                     'stem': stem,
@@ -264,11 +270,9 @@ function getKeyphrases(results, maximum) {
     var score;
     var first;
     var match;
-
     /*
      * Iterate over all grouped important words...
      */
-
     for (keyword in results) {
         matches = results[keyword].matches;
         length = matches.length;
@@ -292,8 +296,7 @@ function getKeyphrases(results, maximum) {
              * If we've detected the same stemmed
              * phrase somewhere.
              */
-
-            if (stemmedPhrase) {
+            if (own.call(stemmedPhrases, phrase.value)) {
                 /*
                  * Add weight per phrase to the score of
                  * the phrase.
diff --git a/test.js b/test.js
index 62ca572..e0fd5a1 100644
--- a/test.js
+++ b/test.js
@@ -20,13 +20,16 @@ var equal = assert.strictEqual;
  * Fixture.
  *
  * First three paragraphs on Term Extraction from Wikipedia:
- * http://en.wikipedia.org/wiki/Terminology_extraction
+ * http://en.wikipedia.org/wiki/Terminology_extraction.
+ *
+ * There’s also some `constructor`s sprinkled throughout the
+ * document to check if prototypal properties work correctly.
  */
 
 var fixture = 'Terminology mining, term extraction, term recognition, or ' +
     'glossary extraction, is a subtask of information extraction. ' +
     'The goal of terminology extraction is to automatically extract ' +
-    'relevant terms from a given corpus.' +
+    'relevant terms from a given corpus constructor.' +
     '\n\n' +
     'In the semantic web era, a growing number of communities and ' +
     'networked enterprises started to access and interoperate through ' +
@@ -34,14 +37,14 @@ var fixture = 'Terminology mining, term extraction, term recognition, or ' +
     'needs is important for several web applications, like ' +
     'topic-driven web crawlers, web services, recommender systems, ' +
     'etc. The development of terminology extraction is essential to ' +
-    'the language industry.' +
+    'the language industry constructor.' +
     '\n\n' +
     'One of the first steps to model the knowledge domain of a ' +
     'virtual community is to collect a vocabulary of domain-relevant ' +
     'terms, constituting the linguistic surface manifestation of ' +
     'domain concepts. Several methods to automatically extract ' +
     'technical terms from domain-specific document warehouses have ' +
-    'been described in the literature.' +
+    'been described in the literature constructor.' +
     '\n\n' +
     'Typically, approaches to automatic term extraction make use of ' +
     'linguistic processors (part of speech tagging, phrase chunking) ' +
@@ -51,15 +54,15 @@ var fixture = 'Terminology mining, term extraction, term recognition, or ' +
     'and prepositional-NPs "board of directors" - in English, the ' +
     'first two constructs are the most frequent). Terminological ' +
     'entries are then filtered from the candidate list using ' +
-    'statistical and machine learning methods. Once filtered, ' +
+    'statistical and machine learning methods constructor. Once filtered, ' +
     'because of their low ambiguity and high specificity, these terms ' +
     'are particularly useful for conceptualizing a knowledge domain ' +
     'or for supporting the creation of a domain ontology. Furthermore, ' +
     'terminology extraction is a very useful starting point for ' +
     'semantic similarity, knowledge management, human translation ' +
-    'and machine translation, etc.';
+    'and machine translation, etc. constructor.';
 
-var keyScores = [1, 1, 0.71, 0.57, 0.57];
+var keyScores = [1, 1, 0.71, 0.71, 0.57, 0.57];
 var phraseScores = [1, 0.55, 0.53, 0.24, 0.18];
 
 /*
@@ -78,7 +81,7 @@ describe('keywords()', function () {
             assert('keywords' in namespace);
             assert('keyphrases' in namespace);
 
-            equal(namespace.keywords.length, 5);
+            equal(namespace.keywords.length, 6);
             equal(namespace.keyphrases.length, 5);
         });