{"id":19370,"date":"2017-12-01T12:15:34","date_gmt":"2017-12-01T10:15:34","guid":{"rendered":"https:\/\/www.webcodegeeks.com\/?p=19370"},"modified":"2017-11-30T10:52:40","modified_gmt":"2017-11-30T08:52:40","slug":"scikit-learn-creating-matrix-named-entity-counts","status":"publish","type":"post","link":"https:\/\/www.webcodegeeks.com\/python\/scikit-learn-creating-matrix-named-entity-counts\/","title":{"rendered":"scikit-learn: Creating a matrix of named entity counts"},"content":{"rendered":"<p>I\u2019ve been trying to improve my score on Kaggle\u2019s <a href=\"https:\/\/www.kaggle.com\/c\/spooky-author-identification\">Spooky Author Identification<\/a> competition, and my latest idea was building a model which used named entities extracted using the <a href=\"https:\/\/github.com\/aboSamoor\/polyglot\">polyglot NLP library<\/a>.<\/p>\n<p>We\u2019ll start by learning how to extract entities form a sentence using polyglot which isn\u2019t too tricky:<\/p>\n<pre class=\"brush:py\">&gt;&gt;&gt; from polyglot.text import Text\r\n&gt;&gt;&gt; doc = \"My name is David Beckham. Hello from London, England\"\r\n&gt;&gt;&gt; Text(doc, hint_language_code=\"en\").entities\r\n[I-PER(['David', 'Beckham']), I-LOC(['London']), I-LOC(['England'])]<\/pre>\n<p>This sentence contains three entities. We\u2019d like each entity to be a string rather than an array of values so let\u2019s refactor the code to do that:<\/p>\n<pre class=\"brush:py\">&gt;&gt;&gt; [\"_\".join(entity) for entity in Text(doc, hint_language_code=\"en\").entities]\r\n['David_Beckham', 'London', 'England']<\/pre>\n<p>That\u2019s it for the polyglot part of the solution. Now let\u2019s work out how to integrate that with scikit-learn.<\/p>\n<p>I\u2019ve been using scikit-learn\u2019s <cite><a href=\"http:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.pipeline.Pipeline.html\">Pipeline<\/a><\/cite> abstraction for the other models I\u2019ve created so I\u2019d like to take the same approach here. This is an example of a model that creates a matrix of unigram counts and creates a Naive Bayes model on top of that:<\/p>\n<pre class=\"brush:py\">from sklearn.feature_extraction.text import CountVectorizer\r\nfrom sklearn.naive_bayes import MultinomialNB\r\nfrom sklearn.pipeline import Pipeline\r\n\u00a0\r\nnlp_pipeline = Pipeline([\r\n    ('cv', CountVectorizer(),\r\n    ('mnb', MultinomialNB())\r\n])\r\n\u00a0\r\n...\r\n#\u00a0Train and Test the model\r\n...<\/pre>\n<p>I was going to write a class similar to <cite><a href=\"http:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.feature_extraction.text.CountVectorizer.html\">CountVectorizer<\/a><\/cite> but after reading its code for a couple of hours I realised that I could just pass in a custom analyzer instead. This is what I ended up with:<\/p>\n<pre class=\"brush:py\">entities = {}\r\n\u00a0\r\n\u00a0\r\ndef analyze(doc):\r\n    if doc not in entities:\r\n        entities[doc] = [\"_\".join(entity) for entity in Text(doc, hint_language_code=\"en\").entities]\r\n    return entities[doc]\r\n\u00a0\r\nnlp_pipeline = Pipeline([\r\n    ('cv', CountVectorizer(analyzer=lambda doc: analyze(doc))),\r\n    ('mnb', MultinomialNB())\r\n])<\/pre>\n<p>I\u2019m caching the results in a dictionary because the entity extraction is quite time consuming and there\u2019s no point recalculating it each time the function is called.<\/p>\n<p>Unfortunately this model didn\u2019t help me improve my best score. It scores a log loss of around 0.5, a bit worse than the 0.45 I\u2019ve achieved using the unigram model &amp;#55357;&amp;#56897;<\/p>\n<div class=\"attribution\">\n<table>\n<tbody>\n<tr>\n<td>Published on Web Code Geeks with permission by Mark Needham, partner at our <a href=\"http:\/\/www.webcodegeeks.com\/join-us\/wcg\/\" target=\"_blank\" rel=\"noopener\">WCG program<\/a>. See the original article here: <a href=\"http:\/\/www.markhneedham.com\/blog\/2017\/11\/29\/scikit-learn-creating-a-matrix-of-named-entity-counts\/\" target=\"_blank\" rel=\"noopener\">scikit-learn: Creating a matrix of named entity counts<\/a><\/p>\n<p>Opinions expressed by Web Code Geeks contributors are their own.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>I\u2019ve been trying to improve my score on Kaggle\u2019s Spooky Author Identification competition, and my latest idea was building a model which used named entities extracted using the polyglot NLP library. We\u2019ll start by learning how to extract entities form a sentence using polyglot which isn\u2019t too tricky: &gt;&gt;&gt; from polyglot.text import Text &gt;&gt;&gt; doc &hellip;<\/p>\n","protected":false},"author":48,"featured_media":1651,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[53],"tags":[],"class_list":["post-19370","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>scikit-learn: Creating a matrix of named entity counts - Web Code Geeks - 2026<\/title>\n<meta name=\"description\" content=\"I\u2019ve been trying to improve my score on Kaggle\u2019s Spooky Author Identification competition, and my latest idea was building a model which used named\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.webcodegeeks.com\/python\/scikit-learn-creating-matrix-named-entity-counts\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"scikit-learn: Creating a matrix of named entity counts - 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