{"id":551,"date":"2016-09-05T15:46:51","date_gmt":"2016-09-05T15:46:51","guid":{"rendered":"http:\/\/intelligentonlinetools.com\/blog\/?p=551"},"modified":"2017-10-15T01:43:04","modified_gmt":"2017-10-15T01:43:04","slug":"getting-wordnet-information-and-building-and-building-graph-with-python-and-networkx","status":"publish","type":"post","link":"http:\/\/intelligentonlinetools.com\/blog\/2016\/09\/05\/getting-wordnet-information-and-building-and-building-graph-with-python-and-networkx\/","title":{"rendered":"Getting WordNet Information and Building Graph with Python and NetworkX"},"content":{"rendered":"

WordNet and Wikipedia are often utilized in text mining algorithms for enriching short text representation [1] or for extracting additional knowledge about words. [2] WordNet’s structure makes it a useful tool for computational linguistics and natural language processing.[3] In this post we will look how to pull information from WordNet using python. Also we will look how to build graph for relations between words using python and NetworkX.<\/p>\n

WordNet groups English words into sets of synonyms called synsets, provides short definitions and usage examples, and records a number of relations among these synonym sets or their members. WordNet can thus be seen as a combination of dictionary and thesaurus. While it is accessible to human users via a web browser, its primary use is in automatic text analysis and artificial intelligence applications. [4]<\/p>\n

Here is how to get all synsets for the word ‘good’ using NLTK package:<\/p>\n

\r\nfrom nltk.corpus import wordnet as wn\r\n\r\nprint (wn.synsets('good'))\r\n\r\n#This is the output of above line:\r\n#[Synset('good.n.01'), Synset('good.n.02'), Synset('good.n.03'), Synset('commodity.n.01'), Synset('good.a.01'), Synset('full.s.06'), Synset('good.a.03'), Synset('estimable.s.02'), Synset('beneficial.s.01'), Synset('good.s.06'), Synset('good.s.07'), Synset('adept.s.01'), Synset('good.s.09'), Synset('dear.s.02'), Synset('dependable.s.04'), Synset('good.s.12'), Synset('good.s.13'), Synset('effective.s.04'), Synset('good.s.15'), Synset('good.s.16'), Synset('good.s.17'), Synset('good.s.18'), Synset('good.s.19'), Synset('good.s.20'), Synset('good.s.21'), Synset('well.r.01'), Synset('thoroughly.r.02')]\r\n<\/code><\/pre>\n
All synsets are connected to other synsets by means of semantic relations. These relations, which are not all shared by all lexical categories, include:<\/p>\n
hypernyms: Y is a hypernym of X if every X is a (kind of) Y (canine is a hypernym of dog)
\nhyponyms: Y is a hyponym of X if every Y is a (kind of) X (dog is a hyponym of canine)
\nmeronym: Y is a meronym of X if Y is a part of X (window is a meronym of building)
\nholonym: Y is a holonym of X if X is a part of Y (building is a holonym of window)    [4]<\/p>\n
Here is how can we can get hypernyms and hyponyms from WordNet.<\/p>\n
car = wn.synset(‘car.n.01’)
\nprint (“HYPERNYMS”)
\nprint (car.hypernyms())
\nprint (“HYPONYMS”)
\nprint (car.hyponyms())<\/p>\n
Here is the output from above code:
\nHYPERNYMS
\n[Synset(‘motor_vehicle.n.01’)]
\nHYPONYMS
\n[Synset(‘ambulance.n.01’), Synset(‘beach_wagon.n.01’), Synset(‘bus.n.04’), Synset(‘cab.n.03’), Synset(‘compact.n.03’), Synset(‘convertible.n.01’), Synset(‘coupe.n.01’), Synset(‘cruiser.n.01’), Synset(‘electric.n.01’), Synset(‘gas_guzzler.n.01’), Synset(‘hardtop.n.01’), Synset(‘hatchback.n.01’), Synset(‘horseless_carriage.n.01’), Synset(‘hot_rod.n.01’), Synset(‘jeep.n.01’), Synset(‘limousine.n.01’), Synset(‘loaner.n.02’), Synset(‘minicar.n.01’), Synset(‘minivan.n.01’), Synset(‘model_t.n.01’), Synset(‘pace_car.n.01’), Synset(‘racer.n.02’), Synset(‘roadster.n.01’), Synset(‘sedan.n.01’), Synset(‘sport_utility.n.01’), Synset(‘sports_car.n.01’), Synset(‘stanley_steamer.n.01’), Synset(‘stock_car.n.01’), Synset(‘subcompact.n.01’), Synset(‘touring_car.n.01’), Synset(‘used-car.n.01’)]
\n<\/code><\/p>\n
Here is how to get synonyms, antonyms ,  lemmas and similarity: [5]<\/p>\n
\r\nsynonyms = []\r\nantonyms = []\r\n\r\nfor syn in wn.synsets(\"good\"):\r\n    for l in syn.lemmas():\r\n        synonyms.append(l.name())\r\n        if l.antonyms():\r\n            antonyms.append(l.antonyms()[0].name())\r\n\r\nprint(set(synonyms))\r\nprint(set(antonyms))\r\nprint (syn.lemmas())\r\n\r\n\r\nw1 = wn.synset('ship.n.01')\r\nw2 = wn.synset('cat.n.01')\r\nprint(w1.wup_similarity(w2))\r\n<\/code><\/pre>\n
Here is how we can use Textblob package [6] and represent some word relations via graph. The output graph is shown below.<\/p>\n
\r\nfrom textblob import Word\r\nword = Word(\"plant\")\r\nprint (word.synsets[:5])\r\nprint (word.definitions[:5])\r\n\r\nword = Word(\"computer\")\r\nfor syn in word.synsets:\r\n    for l in syn.lemma_names():\r\n        synonyms.append(l)\r\n        \r\nimport networkx as nx\r\nimport matplotlib.pyplot as plt\r\nG=nx.Graph()\r\n\r\n\r\nw=word.synsets[1]\r\n\r\n\r\nG.add_node(w.name())\r\nfor h in w.hypernyms():\r\n      print (h)\r\n      G.add_node(h.name())\r\n      G.add_edge(w.name(),h.name())\r\n\r\n\r\nfor h in w.hyponyms():\r\n      print (h)\r\n      G.add_node(h.name())\r\n      G.add_edge(w.name(),h.name())\r\n\r\nprint (G.nodes(data=True))\r\nplt.show()\r\nnx.draw(G, width=2, with_labels=True)\r\nplt.savefig(\"path.png\")\r\n<\/code><\/pre>\n
\"Wordnet_graph\"<\/p>\n
Here is the full source code<\/p>\n
\r\nfrom nltk.corpus import wordnet as wn\r\n\r\nprint (wn.synsets('good'))\r\n\r\ncar = wn.synset('car.n.01')\r\nprint (\"HYPERNYMS\")\r\nprint (car.hypernyms())\r\nprint (\"HYPONYMS\")\r\nprint (car.hyponyms())\r\n\r\nsynonyms = []\r\nantonyms = []\r\n\r\nfor syn in wn.synsets(\"good\"):\r\n    for l in syn.lemmas():\r\n        synonyms.append(l.name())\r\n        if l.antonyms():\r\n            antonyms.append(l.antonyms()[0].name())\r\n\r\nprint(set(synonyms))\r\nprint(set(antonyms))\r\nprint (syn.lemmas())\r\n\r\n\r\n\r\nw1 = wn.synset('ship.n.01')\r\nw2 = wn.synset('cat.n.01')\r\nprint(w1.wup_similarity(w2))\r\n\r\n\r\nfrom textblob import Word\r\nword = Word(\"plant\")\r\nprint (word.synsets[:5])\r\nprint (word.definitions[:5])\r\n\r\nword = Word(\"computer\")\r\nfor syn in word.synsets:\r\n    for l in syn.lemma_names():\r\n        synonyms.append(l)\r\n\r\n\r\nimport networkx as nx\r\nimport matplotlib.pyplot as plt\r\nG=nx.Graph()\r\n\r\n\r\nw=word.synsets[1]\r\n\r\n\r\nG.add_node(w.name())\r\nfor h in w.hypernyms():\r\n      print (h)\r\n      G.add_node(h.name())\r\n      G.add_edge(w.name(),h.name())\r\n     \r\n\r\n\r\n\r\nfor h in w.hyponyms():\r\n      print (h)\r\n      G.add_node(h.name())\r\n      G.add_edge(w.name(),h.name())\r\n\r\n\r\n\r\nprint (G.nodes(data=True))\r\nplt.show()\r\nnx.draw(G, width=2, with_labels=True)\r\nplt.savefig(\"path.png\")\r\n\r\n<\/code><\/pre>\n
References<\/strong>
\n1. Enriching short text representation in microblog for clustering<\/a>
\n2. Automatic Topic Hierarchy Generation Using WordNet<\/a>
\n3.  WordNet <\/a>
\n4.  WordNet <\/a>
\n5.  WordNet NLTK Tutorial <\/a>
\n6. Tutorial: What is WordNet? A Conceptual Introduction Using Python<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
WordNet and Wikipedia are often utilized in text mining algorithms for enriching short text representation [1] or for extracting additional knowledge about words. [2] WordNet’s structure makes it a useful tool for computational linguistics and natural language processing.[3] In this post we will look how to pull information from WordNet using python. Also we will … Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":[]},"categories":[10],"tags":[],"jetpack_publicize_connections":[],"yoast_head":"\nGetting WordNet Information and Building Graph with Python and NetworkX - Machine Learning Applications<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/intelligentonlinetools.com\/blog\/2016\/09\/05\/getting-wordnet-information-and-building-and-building-graph-with-python-and-networkx\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Getting WordNet Information and Building Graph with Python and NetworkX - Machine Learning Applications\" \/>\n<meta property=\"og:description\" content=\"WordNet and Wikipedia are often utilized in text mining algorithms for enriching short text representation [1] or for extracting additional knowledge about words. 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