{"id":1427,"date":"2019-02-03T16:00:20","date_gmt":"2019-02-03T10:30:20","guid":{"rendered":"https:\/\/www.rangakrish.com\/?p=1427"},"modified":"2019-02-03T16:03:20","modified_gmt":"2019-02-03T10:33:20","slug":"coreference-resolution-using-spacy","status":"publish","type":"post","link":"https:\/\/www.rangakrish.com\/index.php\/2019\/02\/03\/coreference-resolution-using-spacy\/","title":{"rendered":"Coreference Resolution Using spaCy"},"content":{"rendered":"

According to Stanford NLP Group<\/a>, “Coreference resolution is the task of finding all expressions that refer to the same entity in a text”<\/strong><\/em>.\u00a0 <\/span>You can also read this Wikipedia page<\/a>.<\/p>\n

For example, in the sentence “Tom dropped the glass jar by accident and broke it”<\/strong><\/em>, what does “it”<\/strong><\/em> refer to? I am sure you will immediately say that “it”<\/strong><\/em> refers to “the glass jar”<\/strong><\/em>. This is a simple example of coreference resolution.<\/p>\n

It can be much more tricky in some cases, but humans usually have no difficulty in resolving coreferences. In today’s article, I want to take a look at the “neuralcoref<\/a>“<\/strong><\/em>\u00a0Python library that is integrated into spaCy’s<\/strong><\/em><\/a> NLP pipeline and hence seamlessly extends spaCy<\/strong><\/em>. You may also want to read this article<\/strong><\/em><\/a>.<\/p>\n

Installing the library is simple. Just follow the instructions given here<\/strong><\/em><\/a>. I have heard that it does not work well some versions of spaCy<\/strong>, but my version of spaCy<\/strong><\/em> (ver 2.0.13) had no compatibility issues.<\/p>\n

We need to import spaCy<\/strong><\/em> and load the relevant coreference model. I chose the “large”<\/strong><\/em> model just to be safe.<\/p>\n

\"Loading<\/a>
Loading the Coreference Model<\/strong><\/figcaption><\/figure>\n

I wrote two simple functions, one to print all the coreference “mentions”<\/strong><\/em> in the document, and another to print the resolved pronoun references.<\/p>\n

\"Functions\"<\/a>
Functions<\/strong><\/em><\/figcaption><\/figure>\n

The full source code is available here<\/strong><\/em><\/a>.<\/p>\n

Let us start with a simple example:<\/p>\n

“My sister has a dog and she loves him.”<\/b><\/span><\/p><\/blockquote>\n

Let us see how the library resolves the references:<\/p>\n

\"Output\"<\/a>
Output<\/strong><\/figcaption><\/figure>\n

That is good. The pronouns are mapped correctly, as we expect.<\/p>\n

Let us extend the above example with another sentence:<\/p>\n

“My sister has a dog and she loves him. He is cute.”<\/b><\/span><\/p><\/blockquote>\n

No major challenge here. Let us see what the library does:<\/p>\n

\"Output\"<\/a>
Output<\/strong><\/figcaption><\/figure>\n

Great. The references are correct.<\/p>\n

In the above examples, we implicitly assumed that the dog is male. What if we use the pronoun “her”<\/strong><\/em> instead of “him”<\/strong><\/em>, implying the dog is a female?<\/p>\n

“My sister has a dog and she loves her.”<\/b><\/span><\/p><\/blockquote>\n

This is the output we get in this case:<\/p>\n

\"Output\"<\/a>
Output<\/strong><\/figcaption><\/figure>\n

Clearly, the mapping of “her”<\/strong><\/em> to “My sister”<\/strong><\/em> is not what we expect. Could it be because of some confusion caused by both objects being females? Let us try a modified example:<\/p>\n

“My brother has a dog and he loves her.”<\/b><\/span><\/p><\/blockquote>\n

In this case, since “brother”<\/strong><\/em> refers to a male, we expect that “her”<\/strong><\/em> should be easily resolved to the only other object, “dog”<\/strong><\/em>. Let us see:<\/p>\n

\"Output\"<\/a>
Output<\/strong><\/figcaption><\/figure>\n

Strange! Even in this case, the library incorrectly maps “her”<\/strong><\/em> to “My brother”<\/strong><\/em>. Such errors could be due to the model, or because the machine learning approach does not lead to “understanding”<\/strong><\/em> in the way we humans understand text.<\/p>\n

Let us try another example:<\/p>\n

“Mary and Julie are sisters. They love chocolates.<\/b>“<\/span><\/p><\/blockquote>\n

In this case, we expect “they”<\/strong><\/em> to refer to both “Mary and Julie”<\/strong><\/em> and not just one of them. Here is the output:<\/p>\n

\"Output\"<\/a>
Output<\/strong><\/figcaption><\/figure>\n

That is nice. Works as expected. Let us introduce a twist in this pattern:<\/p>\n

“John and Mary are neighbours. She admires him because he works hard.<\/b>“<\/span><\/p><\/blockquote>\n

We know that “John”<\/strong><\/em> is male and “Mary”<\/strong><\/em> is female, so “She”<\/strong><\/em> should map to “Mary”<\/strong><\/em> and both “him”<\/strong><\/em> and “he”<\/strong><\/em> should point to “John”<\/strong><\/em>. How does the library handle this?<\/p>\n

\"Output\"<\/a>
Output<\/strong><\/figcaption><\/figure>\n

That is a pleasant surprise! The system resolved the pronouns correctly.<\/p>\n

Wait. What if we use abstract names such as “X”<\/strong><\/em> and “Y”<\/strong><\/em> instead of “John”<\/strong><\/em> and “Mary”<\/strong><\/em>?<\/p>\n

“X and Y are neighbours. She admires him because he works hard.<\/b>“<\/span><\/p><\/blockquote>\n

This is what the library does in this case:<\/p>\n

\"Output\"<\/a>
Output<\/strong><\/figcaption><\/figure>\n

Let us think for a minute here. How would we, as humans, have handled this case? I feel it is acceptable if “She”<\/strong><\/em> maps to “X”<\/strong><\/em> and both “him”<\/strong><\/em> and “he”<\/strong><\/em> map to “Y”<\/strong><\/em>, or the other way around. But the way the library has resolved the references stumps me!<\/p>\n

Let us try one last example:<\/p>\n

“The dog chased the cat. But it escaped.”<\/b><\/span><\/p><\/blockquote>\n

This is what we get:<\/p>\n

\"Output\"<\/a>
Output<\/strong><\/figcaption><\/figure>\n

I think we, as humans, would have mapped “it”<\/strong><\/em> to the “cat”<\/strong><\/em>\u00a0without much effort, because we understand the context. Erroneous mappings from the system appear unavoidable, given that the system does not “understand”<\/strong><\/em> the meaning of the sentence.\u00a0As I mentioned earlier in the\u00a0 <\/span>article, Coreference resolution<\/strong><\/em> is a complex task and I expect that\u00a0neuralcoref<\/strong><\/em> library and other similar systems will become better in due course.<\/p>\n

In case you haven’t noticed, all the examples I have considered for this article involve one type of coreference called\u00a0“anaphora”<\/strong><\/em>. There are other types<\/strong><\/em><\/a>, which can be even more difficult to handle.<\/p>\n

It would be interesting to compare the performance of other libraries such as OpenNLP<\/strong><\/em><\/a>\u00a0and Stanford Parser<\/strong><\/em><\/a>\u00a0on the same set of examples. Well, that is for another article.<\/p>\n

You can download my Python code from here<\/strong><\/em><\/a>. Have a nice weekend!<\/p>\n

\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

According to Stanford NLP Group, “Coreference resolution is the task of finding all expressions that refer to the same entity in a text”.\u00a0 You can also read this Wikipedia page. For example, in the sentence “Tom dropped the glass jar by accident and broke it”, what does “it” refer to? I am sure you will […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[78,107,17,103],"tags":[188,186,185,187],"class_list":["post-1427","post","type-post","status-publish","format-standard","hentry","category-machine-learning","category-natural-language-processing","category-programming","category-python","tag-anaphora","tag-coreference-resolution","tag-neuralcoref","tag-spacy"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p9OLnF-n1","jetpack-related-posts":[{"id":1444,"url":"https:\/\/www.rangakrish.com\/index.php\/2019\/02\/10\/coreference-resolution-in-stanford-corenlp\/","url_meta":{"origin":1427,"position":0},"title":"Coreference Resolution in Stanford CoreNLP","author":"admin","date":"February 10, 2019","format":false,"excerpt":"In the last article, I showed how we can use the neuralcoref\u00a0library along with spaCy\u00a0to do coreference resolution (examples involved anaphoric references). In today's article, I want to try the same (well, almost) examples in Stanford CoreNLP engine and see how they compare. Since CoreNLP is a Java implementation, I\u2026","rel":"","context":"In "Natural Language Processing"","block_context":{"text":"Natural Language Processing","link":"https:\/\/www.rangakrish.com\/index.php\/category\/natural-language-processing\/"},"img":{"alt_text":"Comparison Table","src":"https:\/\/i0.wp.com\/www.rangakrish.com\/wp-content\/uploads\/2019\/02\/Table.jpg?resize=350%2C200&ssl=1","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/www.rangakrish.com\/wp-content\/uploads\/2019\/02\/Table.jpg?resize=350%2C200&ssl=1 1x, https:\/\/i0.wp.com\/www.rangakrish.com\/wp-content\/uploads\/2019\/02\/Table.jpg?resize=525%2C300&ssl=1 1.5x, https:\/\/i0.wp.com\/www.rangakrish.com\/wp-content\/uploads\/2019\/02\/Table.jpg?resize=700%2C400&ssl=1 2x"},"classes":[]},{"id":912,"url":"https:\/\/www.rangakrish.com\/index.php\/2018\/04\/22\/question-answering-using-dependency-trees\/","url_meta":{"origin":1427,"position":1},"title":"Question Answering\u00a0Using Dependency Trees","author":"admin","date":"April 22, 2018","format":false,"excerpt":"A few weeks ago I had written about my brief experiment with Mathematica's new feature, which provides answers to questions based on given text. After that post, I spent some time thinking about how to implement something similar. In today's post, I want to show you what I have been\u2026","rel":"","context":"In "LISP"","block_context":{"text":"LISP","link":"https:\/\/www.rangakrish.com\/index.php\/category\/lisp\/"},"img":{"alt_text":"Dependency Tree","src":"https:\/\/i0.wp.com\/www.rangakrish.com\/wp-content\/uploads\/2018\/04\/Deptree-example.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1368,"url":"https:\/\/www.rangakrish.com\/index.php\/2019\/01\/08\/parsing-text-with-apache-opennlp\/","url_meta":{"origin":1427,"position":2},"title":"Parsing Text with Apache OpenNLP","author":"admin","date":"January 8, 2019","format":false,"excerpt":"In my earlier posts I have written about parsing text using spaCy\u00a0and MeaningCloud's parsing API. For today's article, I decided to take a look at OpenNLP, an open-source ML-based Java toolkit for parsing natural language text. 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We saw how a powerful rule-based pattern matching language allowed us to map fragments of unstructured text to custom categories. In today\u2019s post, I want to go through spaCy\u2019s\u00a0pattern matching capabilities. 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