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[Parsing & Serialization](/categories/parsing) 4. / 5. dennis-de-swart/php-stanford-corenlp-adapter ActiveLibrary[Parsing & Serialization](/categories/parsing) dennis-de-swart/php-stanford-corenlp-adapter ============================================ PHP adapter for use with Stanford CoreNLP tools 7.1.0(7y ago)262.9k↓100%5[2 issues](https://github.com/DennisDeSwart/php-stanford-corenlp-adapter/issues)MITPHPPHP >=5.5 Since Dec 16Pushed 6y ago1 watchersCompare [ Source](https://github.com/DennisDeSwart/php-stanford-corenlp-adapter)[ Packagist](https://packagist.org/packages/dennis-de-swart/php-stanford-corenlp-adapter)[ Docs](https://github.com/DennisDeSwart/PHP-stanford-corenlp-adapter)[ RSS](/packages/dennis-de-swart-php-stanford-corenlp-adapter/feed)WikiDiscussions master Synced 2mo ago READMEChangelog (4)Dependencies (1)Versions (5)Used By (0) PHP Stanford CoreNLP adapter ============================ [](#php-stanford-corenlp-adapter) [![Version](https://camo.githubusercontent.com/328a94f6ab28af5dc2eda61c72004a9af2882fa4546e22ebc24fc709ec9d13ae/68747470733a2f2f696d672e736869656c64732e696f2f7061636b61676973742f762f64656e6e69732d64652d73776172742f7068702d7374616e666f72642d636f72656e6c702d616461707465722e7376673f7374796c653d666c61742d737175617265)](https://packagist.org/packages/dennis-de-swart/php-stanford-corenlp-adapter)[![Total Downloads](https://camo.githubusercontent.com/3cf4ffdd46963c29de8f3f01406d3c8ad9c09c99f5264fdc2ed4f08e1e218968/68747470733a2f2f696d672e736869656c64732e696f2f7061636b61676973742f64742f64656e6e69732d64652d73776172742f7068702d7374616e666f72642d636f72656e6c702d616461707465722e7376673f7374796c653d666c61742d737175617265)](https://packagist.org/packages/dennis-de-swart/php-stanford-corenlp-adapter)[![Maintenance](https://camo.githubusercontent.com/103880b37e0f73f7e9171ab789edfde215270e80ef6e227871e0d210e2dd3127/68747470733a2f2f696d672e736869656c64732e696f2f6d61696e74656e616e63652f7965732f323032302e7376673f7374796c653d666c61742d737175617265)](https://github.com/DennisDeSwart/php-stanford-corenlp-adapter)[![Minimum PHP Version](https://camo.githubusercontent.com/5a7c0787478081952cc7cf21092df637a2ca9e4d9a34dc81fac6422510a1e31a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7068702d253345253344253230352e362d3441433531432e7376673f7374796c653d666c61742d737175617265)](http://php.net/)[![License](https://camo.githubusercontent.com/8dbf632366025e618694ea64eab4101be8028cc228d6f96f6a379fe8eeed8299/68747470733a2f2f696d672e736869656c64732e696f2f7061636b61676973742f6c2f64656e6e69732d64652d73776172742f7068702d7374616e666f72642d636f72656e6c702d616461707465722e7376673f7374796c653d666c61742d737175617265)](https://opensource.org/licenses/MIT) PHP adapter for use with Stanford CoreNLP Features -------- [](#features) - Connect to Stanford University CoreNLP API online - Connect to Stanford CoreNLP 3.7.0 server - Annotators available: tokenize,ssplit,pos, parse, depparse, ner, regexner,lemma, mention, natlog, coref, openie, kbp - The package creates Part-Of-Speech Trees with depth, parent- and child ID Requirements ------------ [](#requirements) - PHP 5.5 or higher: it also works on PHP 7 - Windows or Linux 64-bit, 8Gb memory or more recommended - Either Guzzle HTTP Client (installed by default) or only cURL. - Composer for PHP ``` https://getcomposer.org/ ``` Update 24th February 2018 ------------------------- [](#update-24th-february-2018) PHP7 Type hinting removed, because it was causing issues for some users. Update 28th January 2019 ------------------------ [](#update-28th-january-2019) Fixed issue with PHP 7.1 upwards Installation using ZIP files ---------------------------- [](#installation-using-zip-files) - Install Stanford CoreNLP Server. See the installation walkthrough below. - Download and unpack the files from this package. - Copy the files to your to your webserver directory. Usually "htdocs" or "var/www". - Run a Composer update Installation using Composer --------------------------- [](#installation-using-composer) - Insert the following line into the "require" of your "composer.json" file. ``` { "require": { "dennis-de-swart/php-stanford-corenlp-adapter": "*" } } ``` - Run a composer update Using the Stanford CoreNLP online API service --------------------------------------------- [](#using-the-stanford-corenlp-online-api-service) The adapter by default uses Stanford's online API service. This should work right after the composer update. Note that the online API is a public service. If you want to analyze large volumes of text or sensitive data, please install the Java server version. OpenIE ------ [](#openie) OpenIE creates "subject-relation-object" tuples. This is similar (but not the same) as the "Subject-Verb-Object" concept of the English language. Notes: - OpenIE is only available on the Java offline version, not with the "online" mode. See the installation walkthrough below - OpenIE data is not always available. Sometimes the result array might show empty, this is not an error. ``` http://nlp.stanford.edu/software/openie.html https://en.wikipedia.org/wiki/Subject-verb-object ``` Installation / Walkthrough for Java server version ================================================== [](#installation--walkthrough-for-java-server-version) Step 1: install Java -------------------- [](#step-1-install-java) ``` https://java.com/en/download/help/index_installing.xml?os=All+Platforms&j=8&n=20 ``` Step 2: installing the Stanford CoreNLP 3.7.0 server ---------------------------------------------------- [](#step-2-installing-the-stanford-corenlp-370-server) ``` http://stanfordnlp.github.io/CoreNLP/index.html#download ``` Step 3: Port for server ----------------------- [](#step-3-port-for-server) Default port for the Java server is port 9000. If port 9000 is not available you can change the port in the "bootstrap.php" file. Example: ``` define('CURLURL' , 'http://localhost:9000/'); ``` Step 4: Start the CoreNLP serve from the command line. ------------------------------------------------------ [](#step-4-start-the-corenlp-serve-from-the-command-line) Go to the download directory, then enter the following command: ``` java -mx8g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 ``` Important note: the Stanford manual says "-mx4g", however I found that this can lead to a Java OutOfMemory error. It is also important to use a 64-bit operating system with at enough memory (8Gb or more recommended) Step 5: Test if the server has started by surfing to it's URL ------------------------------------------------------------- [](#step-5-test-if-the-server-has-started-by-surfing-to-its-url) ``` http://localhost:9000/ ``` When you surf to this URL, you should see the CoreNLP GUI. If you have problems with installation you can check the manual: ``` http://stanfordnlp.github.io/CoreNLP/corenlp-server.html ``` Step 6: Set ONLINE\_API to FALSE -------------------------------- [](#step-6-set-online_api-to-false) In "bootstrap.php" set define('ONLINE\_API' , FALSE). This tells the Adapter to use the Java version Usage examples ============== [](#usage-examples) Instantiate the adapter: ------------------------ [](#instantiate-the-adapter) ``` $coreNLP = new CorenlpAdapter(); ``` To process a text, call the "getOutput" method: ----------------------------------------------- [](#to-process-a-text-call-the-getoutput-method) ``` $text = 'The Golden Gate Bridge was designed by Joseph Strauss.'; $coreNLP->getOutput($text); ``` Note that the first time that you process a text, the server takes about 20 to 30 seconds extra to load definitions. All other calls to the server after that will be much faster. Small texts are usually processed within seconds. The results ----------- [](#the-results) If successful the following properties will be available: ``` $coreNLP->serverMemory; //contains all of the server output $coreNLP->trees; //contains processed flat trees. Each part of the tree is assigned an ID key $coreNLP->getWordValues($coreNLP->trees[1]) // get just the words from a tree ``` --- ### Diagram A: Tree With Tokens [](#diagram-a-tree-with-tokens) --- ``` Array ( [1] => Array ( [parent] => [pennTreebankTag] => ROOT [depth] => 0 ) [2] => Array ( [parent] => 1 [pennTreebankTag] => S [depth] => 2 ) [3] => Array ( [parent] => 2 [pennTreebankTag] => NP [depth] => 4 ) [4] => Array ( [parent] => 3 [pennTreebankTag] => PRP [depth] => 6 [word] => I [index] => 1 [originalText] => I [lemma] => I [characterOffsetBegin] => 0 [characterOffsetEnd] => 1 [pos] => PRP [ner] => O [before] => [after] => [openIE] => Array ( [0] => subject [1] => subject [2] => subject ) ) [5] => Array ( [parent] => 2 [pennTreebankTag] => VP [depth] => 4 ) [6] => Array ( [parent] => 5 [pennTreebankTag] => MD [depth] => 6 [word] => will [index] => 2 [originalText] => will [lemma] => will [characterOffsetBegin] => 2 [characterOffsetEnd] => 6 [pos] => MD [ner] => O [before] => [after] => [openIE] => Array ( [0] => subject [1] => subject [2] => relation ) ) [7] => Array ( [parent] => 5 [pennTreebankTag] => VP [depth] => 6 ) [8] => Array ( [parent] => 7 [pennTreebankTag] => VB [depth] => 8 [word] => meet [index] => 3 [originalText] => meet [lemma] => meet [characterOffsetBegin] => 7 [characterOffsetEnd] => 11 [pos] => VB [ner] => O [before] => [after] => [openIE] => Array ( [0] => subject [1] => subject [2] => relation ) ) [9] => Array ( [parent] => 7 [pennTreebankTag] => NP [depth] => 8 ) [10] => Array ( [parent] => 9 [pennTreebankTag] => NP [depth] => 10 ) [11] => Array ( [parent] => 10 [pennTreebankTag] => NNP [depth] => 12 [word] => Mary [index] => 4 [originalText] => Mary [lemma] => Mary [characterOffsetBegin] => 12 [characterOffsetEnd] => 16 [pos] => NNP [ner] => PERSON [before] => [after] => [openIE] => Array ( [1] => subject [2] => object [3] => subject [0] => subject ) ) [12] => Array ( [parent] => 9 [pennTreebankTag] => PP [depth] => 10 ) [13] => Array ( [parent] => 12 [pennTreebankTag] => IN [depth] => 12 [word] => in [index] => 5 [originalText] => in [lemma] => in [characterOffsetBegin] => 17 [characterOffsetEnd] => 19 [pos] => IN [ner] => O [before] => [after] => [openIE] => Array ( [1] => relation [3] => relation [0] => relation ) ) [14] => Array ( [parent] => 12 [pennTreebankTag] => NP [depth] => 12 ) [15] => Array ( [parent] => 14 [pennTreebankTag] => NNP [depth] => 14 [word] => New [index] => 6 [originalText] => New [lemma] => New [characterOffsetBegin] => 20 [characterOffsetEnd] => 23 [pos] => NNP [ner] => LOCATION [before] => [after] => [openIE] => Array ( [1] => relation [3] => object [0] => object ) ) [16] => Array ( [parent] => 14 [pennTreebankTag] => NNP [depth] => 14 [word] => York [index] => 7 [originalText] => York [lemma] => York [characterOffsetBegin] => 24 [characterOffsetEnd] => 28 [pos] => NNP [ner] => LOCATION [before] => [after] => [openIE] => Array ( [1] => object [3] => object ) ) [17] => Array ( [parent] => 7 [pennTreebankTag] => PP [depth] => 8 ) [18] => Array ( [parent] => 17 [pennTreebankTag] => IN [depth] => 10 [word] => at [index] => 8 [originalText] => at [lemma] => at [characterOffsetBegin] => 29 [characterOffsetEnd] => 31 [pos] => IN [ner] => O [before] => [after] => [openIE] => Array ( [1] => object ) ) [19] => Array ( [parent] => 17 [pennTreebankTag] => NP [depth] => 10 ) [20] => Array ( [parent] => 19 [pennTreebankTag] => CD [depth] => 12 [word] => 10pm [index] => 9 [originalText] => 10pm [lemma] => 10pm [characterOffsetBegin] => 32 [characterOffsetEnd] => 36 [pos] => CD [ner] => TIME [normalizedNER] => T22:00 [before] => [after] => [timex] => Array ( [tid] => t1 [type] => TIME [value] => T22:00 ) [openIE] => Array ( [0] => object [1] => object ) ) ) ``` --- ### Diagram B: The ServerMemory contains all the server data [](#diagram-b-the-servermemory-contains-all-the-server-data) --- ``` Array ( [0] => Array ( [sentences] => Array ( [0] => Array ( [index] => 0 [parse] => (ROOT (S (NP (PRP I)) (VP (MD will) (VP (VB meet) (NP (NP (NNP Mary)) (PP (IN in) (NP (NNP New) (NNP York)))) (PP (IN at) (NP (CD 10pm))))))) [basic-dependencies] => Array ( [0] => Array ( [dep] => ROOT [governor] => 0 [governorGloss] => ROOT [dependent] => 3 [dependentGloss] => meet ) [1] => Array ( [dep] => nsubj [governor] => 3 [governorGloss] => meet [dependent] => 1 [dependentGloss] => I ) [2] => Array ( [dep] => aux [governor] => 3 [governorGloss] => meet [dependent] => 2 [dependentGloss] => will ) [3] => Array ( [dep] => dobj [governor] => 3 [governorGloss] => meet [dependent] => 4 [dependentGloss] => Mary ) [4] => Array ( [dep] => case [governor] => 7 [governorGloss] => York [dependent] => 5 [dependentGloss] => in ) [5] => Array ( [dep] => compound [governor] => 7 [governorGloss] => York [dependent] => 6 [dependentGloss] => New ) [6] => Array ( [dep] => nmod [governor] => 4 [governorGloss] => Mary [dependent] => 7 [dependentGloss] => York ) [7] => Array ( [dep] => case [governor] => 9 [governorGloss] => 10pm [dependent] => 8 [dependentGloss] => at ) [8] => Array ( [dep] => nmod [governor] => 3 [governorGloss] => meet [dependent] => 9 [dependentGloss] => 10pm ) ) [collapsed-dependencies] => Array ( [0] => Array ( [dep] => ROOT [governor] => 0 [governorGloss] => ROOT [dependent] => 3 [dependentGloss] => meet ) [1] => Array ( [dep] => nsubj [governor] => 3 [governorGloss] => meet [dependent] => 1 [dependentGloss] => I ) [2] => Array ( [dep] => aux [governor] => 3 [governorGloss] => meet [dependent] => 2 [dependentGloss] => will ) [3] => Array ( [dep] => dobj [governor] => 3 [governorGloss] => meet [dependent] => 4 [dependentGloss] => Mary ) [4] => Array ( [dep] => case [governor] => 7 [governorGloss] => York [dependent] => 5 [dependentGloss] => in ) [5] => Array ( [dep] => compound [governor] => 7 [governorGloss] => York [dependent] => 6 [dependentGloss] => New ) [6] => Array ( [dep] => nmod:in [governor] => 4 [governorGloss] => Mary [dependent] => 7 [dependentGloss] => York ) [7] => Array ( [dep] => case [governor] => 9 [governorGloss] => 10pm [dependent] => 8 [dependentGloss] => at ) [8] => Array ( [dep] => nmod:at [governor] => 3 [governorGloss] => meet [dependent] => 9 [dependentGloss] => 10pm ) ) [collapsed-ccprocessed-dependencies] => Array ( [0] => Array ( [dep] => ROOT [governor] => 0 [governorGloss] => ROOT [dependent] => 3 [dependentGloss] => meet ) [1] => Array ( [dep] => nsubj [governor] => 3 [governorGloss] => meet [dependent] => 1 [dependentGloss] => I ) [2] => Array ( [dep] => aux [governor] => 3 [governorGloss] => meet [dependent] => 2 [dependentGloss] => will ) [3] => Array ( [dep] => dobj [governor] => 3 [governorGloss] => meet [dependent] => 4 [dependentGloss] => Mary ) [4] => Array ( [dep] => case [governor] => 7 [governorGloss] => York [dependent] => 5 [dependentGloss] => in ) [5] => Array ( [dep] => compound [governor] => 7 [governorGloss] => York [dependent] => 6 [dependentGloss] => New ) [6] => Array ( [dep] => nmod:in [governor] => 4 [governorGloss] => Mary [dependent] => 7 [dependentGloss] => York ) [7] => Array ( [dep] => case [governor] => 9 [governorGloss] => 10pm [dependent] => 8 [dependentGloss] => at ) [8] => Array ( [dep] => nmod:at [governor] => 3 [governorGloss] => meet [dependent] => 9 [dependentGloss] => 10pm ) ) [openie] => Array ( [0] => Array ( [subject] => I [subjectSpan] => Array ( [0] => 0 [1] => 1 ) [relation] => will meet Mary at [relationSpan] => Array ( [0] => 1 [1] => 3 ) [object] => 10pm [objectSpan] => Array ( [0] => 8 [1] => 9 ) ) [1] => Array ( [subject] => I [subjectSpan] => Array ( [0] => 0 [1] => 1 ) [relation] => will meet [relationSpan] => Array ( [0] => 1 [1] => 3 ) [object] => Mary in New York [objectSpan] => Array ( [0] => 3 [1] => 7 ) ) [2] => Array ( [subject] => I [subjectSpan] => Array ( [0] => 0 [1] => 1 ) [relation] => will meet [relationSpan] => Array ( [0] => 1 [1] => 3 ) [object] => Mary [objectSpan] => Array ( [0] => 3 [1] => 4 ) ) [3] => Array ( [subject] => Mary [subjectSpan] => Array ( [0] => 3 [1] => 4 ) [relation] => is in [relationSpan] => Array ( [0] => 4 [1] => 5 ) [object] => New York [objectSpan] => Array ( [0] => 5 [1] => 7 ) ) ) [tokens] => Array ( [0] => Array ( [index] => 1 [word] => I [originalText] => I [lemma] => I [characterOffsetBegin] => 0 [characterOffsetEnd] => 1 [pos] => PRP [ner] => O [before] => [after] => ) [1] => Array ( [index] => 2 [word] => will [originalText] => will [lemma] => will [characterOffsetBegin] => 2 [characterOffsetEnd] => 6 [pos] => MD [ner] => O [before] => [after] => ) [2] => Array ( [index] => 3 [word] => meet [originalText] => meet [lemma] => meet [characterOffsetBegin] => 7 [characterOffsetEnd] => 11 [pos] => VB [ner] => O [before] => [after] => ) [3] => Array ( [index] => 4 [word] => Mary [originalText] => Mary [lemma] => Mary [characterOffsetBegin] => 12 [characterOffsetEnd] => 16 [pos] => NNP [ner] => PERSON [before] => [after] => ) [4] => Array ( [index] => 5 [word] => in [originalText] => in [lemma] => in [characterOffsetBegin] => 17 [characterOffsetEnd] => 19 [pos] => IN [ner] => O [before] => [after] => ) [5] => Array ( [index] => 6 [word] => New [originalText] => New [lemma] => New [characterOffsetBegin] => 20 [characterOffsetEnd] => 23 [pos] => NNP [ner] => LOCATION [before] => [after] => ) [6] => Array ( [index] => 7 [word] => York [originalText] => York [lemma] => York [characterOffsetBegin] => 24 [characterOffsetEnd] => 28 [pos] => NNP [ner] => LOCATION [before] => [after] => ) [7] => Array ( [index] => 8 [word] => at [originalText] => at [lemma] => at [characterOffsetBegin] => 29 [characterOffsetEnd] => 31 [pos] => IN [ner] => O [before] => [after] => ) [8] => Array ( [index] => 9 [word] => 10pm [originalText] => 10pm [lemma] => 10pm [characterOffsetBegin] => 32 [characterOffsetEnd] => 36 [pos] => CD [ner] => TIME [normalizedNER] => T22:00 [before] => [after] => [timex] => Array ( [tid] => t1 [type] => TIME [value] => T22:00 ) ) ) ) ) ) ``` Any questions? -------------- [](#any-questions) Please let me know. Credits ------- [](#credits) Some functions are forked from this "Stanford parser" package: ``` https://github.com/agentile/PHP-Stanford-NLP ``` ### Health Score 33 — LowBetter than 75% of packages Maintenance18 Infrequent updates — may be unmaintained Popularity28 Limited adoption so far Community10 Small or concentrated contributor base Maturity61 Established project with proven stability Bus Factor1 Top contributor holds 100% of commits — single point of failure How is this calculated?**Maintenance (25%)** — Last commit recency, latest release date, and issue-to-star ratio. Uses a 2-year decay window. **Popularity (30%)** — Total and monthly downloads, GitHub stars, and forks. Logarithmic scaling prevents top-heavy scores. **Community (15%)** — Contributors, dependents, forks, watchers, and maintainers. Measures real ecosystem engagement. **Maturity (30%)** — Project age, version count, PHP version support, and release stability. ### Release Activity Cadence Every ~257 days Total 4 Last Release 2662d ago Major Versions 4.0.0 → 5.2.02017-01-22 5.3.0 → 7.1.02019-01-28 PHP version history (2 changes)4.0.0PHP >=5.3.0 5.2.0PHP >=5.5 ### Community Maintainers ![](https://www.gravatar.com/avatar/ecf47b59f20e487b92ee5648a96da7a4551bb548213dc753918f3517279121e8?d=identicon)[DennisDeSwart](/maintainers/DennisDeSwart) --- Top Contributors [![DennisDeSwart](https://avatars.githubusercontent.com/u/9338304?v=4)](https://github.com/DennisDeSwart "DennisDeSwart (63 commits)") --- Tags composercorenlp-serverlemmanernlpopeniestanford-corenlptokenizerparserposnlpnerstanford ### Embed Badge ![Health badge](/badges/dennis-de-swart-php-stanford-corenlp-adapter/health.svg) ``` [![Health](https://phpackages.com/badges/dennis-de-swart-php-stanford-corenlp-adapter/health.svg)](https://phpackages.com/packages/dennis-de-swart-php-stanford-corenlp-adapter) ``` ### Alternatives [agentile/php-stanford-nlp PHP interface to Stanford NLP Tools 16557.1k](/packages/agentile-php-stanford-nlp)[paquettg/php-html-parser An HTML DOM parser. 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