{"id":60,"date":"2018-01-30T15:10:25","date_gmt":"2018-01-30T15:10:25","guid":{"rendered":"http:\/\/www.cs.jhu.edu\/~mdredze\/wordpress\/?page_id=60"},"modified":"2018-02-06T02:26:40","modified_gmt":"2018-02-06T02:26:40","slug":"code","status":"publish","type":"page","link":"https:\/\/www.cs.jhu.edu\/~mdredze\/code\/","title":{"rendered":"Code"},"content":{"rendered":"<section id=\"huge_it_portfolio_content_2\"\r\n         style=\"clear: both\"\r\n         class=\"portfolio-gallery-content \"\r\n         data-portfolio-id=\"2\">\r\n\t<div id=\"huge-it-container-loading-overlay_2\"><\/div>\r\n\t\t\t\t\t\t<div id=\"huge_it_portfolio_container_2\"\r\n\t     data-show-loading=\"on\"\r\n\t     data-show-center=\"off\"\r\n\t     class=\"huge_it_portfolio_container super-list variable-sizes clearfix view-full-width\" >\r\n\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"Neural UMLS Concept Linker\" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2022-11-21 23:56:24<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">54<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2022\/11\/4882466.png\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group1-2\"\r\n                                       data-description=\" Given a clinical note and span (mention) annotations over the note, this system normalizes each span to it&#039;s corresponding UMLS Concept with high accuracy.\r\n\r\n Elliot Schumacher, Andriy Mulyar, Mark Dredze. Clinical Concept Linking with Contextualized Neural Representations. Association for Computational Linguistics (ACL), 2020.\"\r\n\t\t\t\t\t\t\t\t\t   title=\"Neural UMLS Concept Linker\" data-groupID=\"1-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"Neural UMLS Concept Linker\"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img0\"\r\n                                            data-title=\" 4882466\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2022\/11\/4882466.png\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >Neural UMLS Concept Linker<\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>Given a clinical note and span (mention) annotations over the note, this system normalizes each span to it's corresponding UMLS Concept with high accuracy.\r\n\r\n Elliot Schumacher, Andriy Mulyar, Mark Dredze. Clinical Concept Linking with Contextualized Neural Representations. Association for Computational Linguistics (ACL), 2020.<\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"button-block\">\r\n\t\t\t\t\t\t\t\t<a href=\"https:\/\/github.com\/elliotschu\/clinical-concept-linking\" >View More<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"Cross-Lingual Entity Linker\" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2022-11-21 23:55:56<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">53<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2022\/11\/4882466.png\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group2-2\"\r\n                                       data-description=\" The architecture is described in Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking, Schumacher et al 2020 (to Appear in Findings of ACL 2021, https:\/\/arxiv.org\/abs\/2010.09828).\"\r\n\t\t\t\t\t\t\t\t\t   title=\"Cross-Lingual Entity Linker\" data-groupID=\"2-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"Cross-Lingual Entity Linker\"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img1\"\r\n                                            data-title=\" 4882466\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2022\/11\/4882466.png\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >Cross-Lingual Entity Linker<\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>The architecture is described in Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking, Schumacher et al 2020 (to Appear in Findings of ACL 2021, https:\/\/arxiv.org\/abs\/2010.09828).<\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"button-block\">\r\n\t\t\t\t\t\t\t\t<a href=\"https:\/\/github.com\/elliotschu\/crosslingual-el\" >View More<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"Do Models of Mental Health Based on Social Media Data Generalize?\" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2020-10-30 12:10:07<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">49<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2018\/12\/weaponized_twitter.png\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group3-2\"\r\n                                       data-description=\" Code and data for &quot;Do Models of Mental Health Based on Social Media Data Generalize?&quot; in Findings of EMNLP (2020)\"\r\n\t\t\t\t\t\t\t\t\t   title=\"Do Models of Mental Health Based on Social Media Data Generalize?\" data-groupID=\"3-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"Do Models of Mental Health Based on Social Media Data Generalize?\"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img2\"\r\n                                            data-title=\" weaponized_twitter\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2018\/12\/weaponized_twitter.png\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >Do Models of Mental Health Based on Social Media Data Generalize?<\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>Code and data for \"Do Models of Mental Health Based on Social Media Data Generalize?\" in Findings of EMNLP (2020)<\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"button-block\">\r\n\t\t\t\t\t\t\t\t<a href=\"https:\/\/github.com\/kharrigian\/emnlp-2020-mental-health-generalization\" >View More<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"semantic-text-similarity with BERT\" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2020-02-10 17:03:00<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">47<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2020\/02\/papers.jpg\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group4-2\"\r\n                                       data-description=\" An easy-to-use interface to fine-tuned BERT models for computing semantic similarity. \r\n\r\nThis was used for our submission to the N2C2 2019 shared task.\r\nhttps:\/\/n2c2.dbmi.hms.harvard.edu\/track1\"\r\n\t\t\t\t\t\t\t\t\t   title=\"semantic-text-similarity with BERT\" data-groupID=\"4-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"semantic-text-similarity with BERT\"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img3\"\r\n                                            data-title=\" papers\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2020\/02\/papers.jpg\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >semantic-text-similarity with BERT<\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>An easy-to-use interface to fine-tuned BERT models for computing semantic similarity. \r\n\r\nThis was used for our submission to the N2C2 2019 shared task.\r\nhttps:\/\/n2c2.dbmi.hms.harvard.edu\/track1<\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"button-block\">\r\n\t\t\t\t\t\t\t\t<a href=\"https:\/\/github.com\/AndriyMulyar\/semantic-text-similarity\" >View More<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"BERT Long Document Classification for Clinical Phenotyping\" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2020-02-10 17:01:08<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">46<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2020\/02\/bert.jpg\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group5-2\"\r\n                                       data-description=\" An easy-to-use interface to fully trained BERT based models for multi-class and multi-label long document classification. Pre-trained models are currently available for two clinical note (EHR) phenotyping tasks: smoker identification and obesity detection.\r\n\r\nThis model was used in our 2019 ML4H workshop paper: https:\/\/arxiv.org\/abs\/1910.13664\"\r\n\t\t\t\t\t\t\t\t\t   title=\"BERT Long Document Classification for Clinical Phenotyping\" data-groupID=\"5-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"BERT Long Document Classification for Clinical Phenotyping\"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img4\"\r\n                                            data-title=\" bert\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2020\/02\/bert.jpg\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >BERT Long Document Classification for Clinical Phenotyping<\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>An easy-to-use interface to fully trained BERT based models for multi-class and multi-label long document classification. Pre-trained models are currently available for two clinical note (EHR) phenotyping tasks: smoker identification and obesity detection.\r\n\r\nThis model was used in our 2019 ML4H workshop paper: https:\/\/arxiv.org\/abs\/1910.13664<\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"button-block\">\r\n\t\t\t\t\t\t\t\t<a href=\"https:\/\/github.com\/AndriyMulyar\/bert_document_classification\" >View More<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"Deep DMR\" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2018-03-06 23:18:31<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">41<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2018\/03\/deep-dmr.jpg\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group6-2\"\r\n                                       data-description=\" Implementation of Deep Dirichlet Multinomial Regression in python + cython.\r\n\r\nAdrian Benton, Mark Dredze. Deep Dirichlet Multinomial Regression. North American Chapter of the Association for Computational Linguistics (NAACL), 2018.\r\n\"\r\n\t\t\t\t\t\t\t\t\t   title=\"Deep DMR\" data-groupID=\"6-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"Deep DMR\"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img5\"\r\n                                            data-title=\" deep dmr\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2018\/03\/deep-dmr-1024x768.jpg\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >Deep DMR<\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>Implementation of Deep Dirichlet Multinomial Regression in python + cython.\r\n\r\nAdrian Benton, Mark Dredze. Deep Dirichlet Multinomial Regression. North American Chapter of the Association for Computational Linguistics (NAACL), 2018.\r\n<\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"button-block\">\r\n\t\t\t\t\t\t\t\t<a href=\"https:\/\/github.com\/abenton\/deep-dmr\" >View More<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"Demographer: Gender Identification for Social Media\" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2018-02-05 21:24:03<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">10<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wordpress\/wp-content\/uploads\/2018\/02\/demographer.png\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group7-2\"\r\n                                       data-description=\" Demographer is a Python package that identifies demographic characteristics based on a name. It&#039;s designed for Twitter, where it takes the name of the user and returns information about his or her likely demographics. \"\r\n\t\t\t\t\t\t\t\t\t   title=\"Demographer: Gender Identification for Social Media\" data-groupID=\"7-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"Demographer: Gender Identification for Social Media\"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img6\"\r\n                                            data-title=\" demographer\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2018\/02\/demographer.png\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >Demographer: Gender Identification for Social Media<\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>Demographer is a Python package that identifies demographic characteristics based on a name. It's designed for Twitter, where it takes the name of the user and returns information about his or her likely demographics. <\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"button-block\">\r\n\t\t\t\t\t\t\t\t<a href=\"https:\/\/bitbucket.org\/mdredze\/demographer\" >View More<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"SPRITE: Structured PRIor Topic modEls\" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2018-02-05 22:48:36<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">20<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wordpress\/wp-content\/uploads\/2018\/02\/Sprite_logo.jpg\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group8-2\"\r\n                                       data-description=\" A general purpose topic modeling package that implements SPRITE from our TACL 2015 paper. The package supports multi-threaded training and makes implementing new models easier:\r\nMichael J Paul, Mark Dredze. SPRITE: Generalizing Topic Models with Structured Priors. Transactions of the Association for Computational Linguistics (TACL), 2015.\r\n&lt;br&gt;\r\nThis also contains a detailed readme and tutorial (see Wiki) on how to use the code.\"\r\n\t\t\t\t\t\t\t\t\t   title=\"SPRITE: Structured PRIor Topic modEls\" data-groupID=\"8-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"SPRITE: Structured PRIor Topic modEls\"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img7\"\r\n                                            data-title=\" Sprite_logo\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2018\/02\/Sprite_logo.jpg\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >SPRITE: Structured PRIor Topic modEls<\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>A general purpose topic modeling package that implements SPRITE from our TACL 2015 paper. The package supports multi-threaded training and makes implementing new models easier:\r\nMichael J Paul, Mark Dredze. SPRITE: Generalizing Topic Models with Structured Priors. Transactions of the Association for Computational Linguistics (TACL), 2015.\r\n<br>\r\nThis also contains a detailed readme and tutorial (see Wiki) on how to use the code.<\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"button-block\">\r\n\t\t\t\t\t\t\t\t<a href=\"https:\/\/bitbucket.org\/adrianbenton\/sprite\" >View More<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"PARMA: A Predicate Argument Aligner\" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2018-02-05 22:44:24<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">19<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wordpress\/wp-content\/uploads\/2018\/02\/Parma_F.C._logo.png\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group9-2\"\r\n                                       data-description=\" PARMA is our predicate argument aligner:\r\nTravis Wolfe, Benjamin Van Durme, Mark Dredze, Nicholas Andrews, Charley Beller, Chris Callison-Burch, Jay DeYoung, Justin Snyder, Jonathan Weese, Tan Xu, Xuchen Yao. PARMA: A Predicate Argument Aligner. Association for Computational Linguistics (ACL) (short paper), 2013.\"\r\n\t\t\t\t\t\t\t\t\t   title=\"PARMA: A Predicate Argument Aligner\" data-groupID=\"9-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"PARMA: A Predicate Argument Aligner\"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img8\"\r\n                                            data-title=\" Parma_F.C._logo\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2018\/02\/Parma_F.C._logo.png\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >PARMA: A Predicate Argument Aligner<\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>PARMA is our predicate argument aligner:\r\nTravis Wolfe, Benjamin Van Durme, Mark Dredze, Nicholas Andrews, Charley Beller, Chris Callison-Burch, Jay DeYoung, Justin Snyder, Jonathan Weese, Tan Xu, Xuchen Yao. PARMA: A Predicate Argument Aligner. Association for Computational Linguistics (ACL) (short paper), 2013.<\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"button-block\">\r\n\t\t\t\t\t\t\t\t<a href=\"https:\/\/github.com\/hltcoe\/parma\" >View More<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"Confidence Weighted Learning Library\" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2018-02-05 22:41:44<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">18<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wordpress\/wp-content\/uploads\/2018\/02\/shapes-37716_1280.png\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group10-2\"\r\n                                       data-description=\" We have collected most of the core algorithms in the confidence weighted learning framework for release as a software library. Please email me for the code.\"\r\n\t\t\t\t\t\t\t\t\t   title=\"Confidence Weighted Learning Library\" data-groupID=\"10-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"Confidence Weighted Learning Library\"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img9\"\r\n                                            data-title=\" shapes-37716_1280\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2018\/02\/shapes-37716_1280-1024x1024.png\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >Confidence Weighted Learning Library<\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>We have collected most of the core algorithms in the confidence weighted learning framework for release as a software library. Please email me for the code.<\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"Golden Horse\" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2018-02-05 21:41:13<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">12<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wordpress\/wp-content\/uploads\/2018\/02\/golden-horse.jpg\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group11-2\"\r\n                                       data-description=\" Code for named entity recognition using embeddings, focused on Chinese social media (Weibo). This code implements the methods in our paper:\r\nNanyun Peng, Mark Dredze. Named Entity Recognition for Chinese Social Media with Jointly  Trained Embeddings. Empirical Methods in Natural Language Processing (EMNLP) (short paper), 2015.\"\r\n\t\t\t\t\t\t\t\t\t   title=\"Golden Horse\" data-groupID=\"11-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"Golden Horse\"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img10\"\r\n                                            data-title=\" golden-horse\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2018\/02\/golden-horse.jpg\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >Golden Horse<\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>Code for named entity recognition using embeddings, focused on Chinese social media (Weibo). This code implements the methods in our paper:\r\nNanyun Peng, Mark Dredze. Named Entity Recognition for Chinese Social Media with Jointly  Trained Embeddings. Empirical Methods in Natural Language Processing (EMNLP) (short paper), 2015.<\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"button-block\">\r\n\t\t\t\t\t\t\t\t<a href=\"https:\/\/github.com\/hltcoe\/golden-horse\" >View More<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"Carmen: Geolocation for Twitter\" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2018-02-05 22:37:44<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">17<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wordpress\/wp-content\/uploads\/2018\/02\/2000px-Red_Fedora.svg_.png\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group12-2\"\r\n                                       data-description=\" Carmen is a library for geolocating tweets. Given a tweet, Carmen will return Location objects that represent a physical location. Carmen uses both coordinates and other information in a tweet to make geolocation decisions. It&#039;s not perfect, but this greatly increases the number of geolocated tweets over what Twitter provides.\r\n\r\nThe Python and Java versions don&#039;t give exactly the same results due to differences in the dependencies. If you use Carmen, please cite:\r\nMark Dredze, Michael J Paul, Shane Bergsma, Hieu Tran. Carmen: A Twitter Geolocation System with Applications to Public Health. AAAI Workshop on Expanding the Boundaries of Health Informatics Using AI (HIAI), 2013. \"\r\n\t\t\t\t\t\t\t\t\t   title=\"Carmen: Geolocation for Twitter\" data-groupID=\"12-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"Carmen: Geolocation for Twitter\"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img11\"\r\n                                            data-title=\" 2000px-Red_Fedora.svg\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2018\/02\/2000px-Red_Fedora.svg_-1024x1024.png\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >Carmen: Geolocation for Twitter<\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>Carmen is a library for geolocating tweets. Given a tweet, Carmen will return Location objects that represent a physical location. Carmen uses both coordinates and other information in a tweet to make geolocation decisions. It's not perfect, but this greatly increases the number of geolocated tweets over what Twitter provides.\r\n\r\nThe Python and Java versions don't give exactly the same results due to differences in the dependencies. If you use Carmen, please cite:\r\nMark Dredze, Michael J Paul, Shane Bergsma, Hieu Tran. Carmen: A Twitter Geolocation System with Applications to Public Health. AAAI Workshop on Expanding the Boundaries of Health Informatics Using AI (HIAI), 2013. <\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"button-block\">\r\n\t\t\t\t\t\t\t\t<a href=\"https:\/\/github.com\/mdredze\/carmen-python\" >View More<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"Twitter Stream Downloader \" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2018-02-05 22:28:58<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">15<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wordpress\/wp-content\/uploads\/2018\/02\/twitter-api.png\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group13-2\"\r\n                                       data-description=\" Code for downloading data using the Twitter streaming API. \"\r\n\t\t\t\t\t\t\t\t\t   title=\"Twitter Stream Downloader \" data-groupID=\"13-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"Twitter Stream Downloader \"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img12\"\r\n                                            data-title=\" twitter-api\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2018\/02\/twitter-api.png\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >Twitter Stream Downloader <\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>Code for downloading data using the Twitter streaming API. <\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"button-block\">\r\n\t\t\t\t\t\t\t\t<a href=\"https:\/\/github.com\/mdredze\/twitter_stream_downloader\" >View More<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"Mingpipe\" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2018-02-05 21:50:45<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">14<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wordpress\/wp-content\/uploads\/2018\/02\/chinese-name-Sun-Zhongshan-Sun-Yat-sen-003-e1478109625530.png\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group14-2\"\r\n                                       data-description=\" Code for Chinese name matching. Given two Chinese person names, assigns a score based on how likely the two names refer to the same person. \"\r\n\t\t\t\t\t\t\t\t\t   title=\"Mingpipe\" data-groupID=\"14-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"Mingpipe\"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img13\"\r\n                                            data-title=\" chinese-name-Sun-Zhongshan-Sun-Yat-sen-003-e1478109625530\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2018\/02\/chinese-name-Sun-Zhongshan-Sun-Yat-sen-003-e1478109625530.png\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >Mingpipe<\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>Code for Chinese name matching. Given two Chinese person names, assigns a score based on how likely the two names refer to the same person. <\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"button-block\">\r\n\t\t\t\t\t\t\t\t<a href=\"https:\/\/github.com\/hltcoe\/mingpipe\" >View More<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"csLDA\" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2018-02-05 21:47:54<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">13<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wordpress\/wp-content\/uploads\/2018\/02\/catala.jpg\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group15-2\"\r\n                                       data-description=\" Cross language topic models based on code-switched documents. Documents can be in different languages and some &quot;glue&quot; documents contain multiple languages. csLDA learns topics for each language and aligns topics across languages. \"\r\n\t\t\t\t\t\t\t\t\t   title=\"csLDA\" data-groupID=\"15-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"csLDA\"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img14\"\r\n                                            data-title=\" catala\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2018\/02\/catala.jpg\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >csLDA<\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>Cross language topic models based on code-switched documents. Documents can be in different languages and some \"glue\" documents contain multiple languages. csLDA learns topics for each language and aligns topics across languages. <\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"button-block\">\r\n\t\t\t\t\t\t\t\t<a href=\"https:\/\/github.com\/VioletPeng\/csLDA\" >View More<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"Multiview Representations of Twitter Users\" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2018-02-05 21:34:45<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">11<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wordpress\/wp-content\/uploads\/2018\/02\/shutterstock_59234440.jpg\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group16-2\"\r\n                                       data-description=\" Code and data for our paper:\r\n\r\nAdrian Benton, Raman Arora, and Mark Dredze. Learning Multiview Representations of Twitter Users. Association for Computational Linguistics (ACL), 2016.\"\r\n\t\t\t\t\t\t\t\t\t   title=\"Multiview Representations of Twitter Users\" data-groupID=\"16-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"Multiview Representations of Twitter Users\"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img15\"\r\n                                            data-title=\" shutterstock_59234440\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2018\/02\/shutterstock_59234440.jpg\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >Multiview Representations of Twitter Users<\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>Code and data for our paper:\r\n\r\nAdrian Benton, Raman Arora, and Mark Dredze. Learning Multiview Representations of Twitter Users. Association for Computational Linguistics (ACL), 2016.<\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"button-block\">\r\n\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/datasets\/multiview_embeddings\/\" >View More<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\t\t\t<div\r\n\t\t\t\tclass=\"portelement portelement_2 colorbox_grouping   \" data-symbol=\"Automated Reviewer Assignment (used in multiple ACL affiliated conferences)\" data-category=\"alkaline-earth\">\r\n\t\t\t\t<div class=\"left-block_2\">\r\n\t\t\t\t\t<div class=\"main-image-block_2 add-H-relative\">\r\n                        <p style=\"display:none;\" class=\"load_date\">2018-02-05 22:52:13<\/p>\r\n                        <p style=\"display:none;\" class=\"number\">21<\/p>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"http:\/\/www.cs.jhu.edu\/~mdredze\/wordpress\/wp-content\/uploads\/2018\/02\/shapes-37716_1280.png\"\r\n\t\t\t\t\t\t\t\t\t   class=\" portfolio-group17-2\"\r\n                                       data-description=\" I authored a process for automatically assigning reviewers to an area for ACL affiliated conferences. The code is freely available for others to use. Let me know if you plan on using this system; I&#039;m happy to answer questions. \"\r\n\t\t\t\t\t\t\t\t\t   title=\"Automated Reviewer Assignment (used in multiple ACL affiliated conferences)\" data-groupID=\"17-2\"><img\r\n\t\t\t\t\t\t\t\t\t\t\talt=\"Automated Reviewer Assignment (used in multiple ACL affiliated conferences)\"\r\n\t\t\t\t\t\t\t\t\t\t\tid=\"wd-cl-img16\"\r\n                                            data-title=\" shapes-37716_1280\"\r\n\t\t\t\t\t\t\t\t\t\t\tsrc=\"https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/uploads\/2018\/02\/shapes-37716_1280-1024x1024.png\"><\/a>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t<div class=\"thumbs-block\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t<ul class=\"thumbs-list_2\">\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/ul>\r\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t<div class=\"right-block\">\r\n\t\t\t\t\t\t\t\t\t\t<div class=\"title-block_2\">\r\n\t\t\t\t\t\t<h3 class=\"name\" >Automated Reviewer Assignment (used in multiple ACL affiliated conferences)<\/h3><\/div>\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"description-block_2\">\r\n\t\t\t\t\t\t\t\t<p>I authored a process for automatically assigning reviewers to an area for ACL affiliated conferences. The code is freely available for others to use. Let me know if you plan on using this system; I'm happy to answer questions. <\/p><\/div>\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"button-block\">\r\n\t\t\t\t\t\t\t\t<a href=\"https:\/\/github.com\/mdredze\/automated_reviewer_assigner\" >View More<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\r\n\t\t\t\r\n\t<\/div>\r\n<\/section><style>\r\n.portelement_2 .play-icon.youtube-icon  {\r\n    background: url(https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/plugins\/portfolio-gallery\/assets\/images\/admin_images\/play.youtube.png) center center no-repeat;\r\n    background-size: 30% 30%;\r\n}\r\n.portelement_2 .play-icon.vimeo-icon  {\r\n    background: url(https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/plugins\/portfolio-gallery\/assets\/images\/admin_images\/play.vimeo.png) center center no-repeat;\r\n    background-size: 30% 30%;\r\n}\r\n.portelement_2 .play-icon {\r\n    position: absolute;\r\n    top: 0px;\r\n    left: 0px;\r\n    width: 100%;\r\n    height: 100%;\r\n}\r\n.add-H-relative {\r\n    position: relative;\r\n}\r\n.add-H-block {\r\n    display: block;\r\n    border: none !important;\r\n    box-shadow: none;\r\n\r\n}\r\n\/***<\/add>***\/\r\n.portelement_2 {\r\n    position: relative;\r\n    width: calc(96% - 2px);\r\n    margin:5px 0px 5px 0px;\r\n    padding:2%;\r\n    clear:both;\r\n    overflow: hidden;\r\n    border:1px solid #dedede;\r\n    background:#f9f9f9;\r\n}\r\n\r\n.portelement_2 div.left-block_2 {\r\n    padding-right: 10px;\r\n    display: inline-block;\r\n    float: left;\r\n}\r\n\r\n.portelement_2 div.left-block_2 .main-image-block_2 {\r\n    clear:both;\r\n    width:240px;\r\n}\r\n\r\n.portelement_2 div.left-block_2 .main-image-block_2 img {\r\n    margin:0px !important;\r\n    padding:0px !important;\r\n    width:240px !important;\r\n    height:auto;\r\n}\r\n\r\n.portelement_2 div.left-block_2 .thumbs-block {\r\n    position:relative;\r\n    margin-top:10px;\r\n    display: inline-block;\r\n}\r\n\r\n.portelement_2 div.left-block_2 .thumbs-block .thumbs-list_2{\r\n    padding: 0 !important;\r\n}\r\n\r\n.portelement_2 div.left-block_2 .thumbs-block ul {\r\n    width:240px;\r\n    height:auto;\r\n    display:table;\r\n    margin:0px;\r\n    padding:0px;\r\n    list-style:none;\r\n}\r\n\r\n.portelement_2 div.left-block_2 .thumbs-block ul li {\r\n    margin:2px 3px 0px 2px;\r\n    padding:0px;\r\n    width:75px;\r\n    height:75px;\r\n    float:left;\r\n}\r\n\r\n.portelement_2 div.left-block_2 .thumbs-block ul li a {\r\n    display:block;\r\n    width:75px;\r\n    height:75px;\r\n    border: none;\r\n    box-shadow: none;\r\n}\r\n\r\n.portelement_2 div.left-block_2 .thumbs-block ul li a img {\r\n    margin:0px !important;\r\n    padding:0px !important;\r\n    width:75px;\r\n    height:75px;\r\n}\r\n\r\n.portelement_2 div.right-block {\r\n    vertical-align:top;\r\n    float: right;\r\n    display: inline-block;\r\n    width: calc(96% - 240px);\r\n}\r\n\r\n.portelement_2 div.right-block > div {\r\n    width:100%;\r\n    padding-bottom:10px;\r\n    margin-top:10px;\r\n    background:url('https:\/\/www.cs.jhu.edu\/~mdredze\/wp-content\/plugins\/portfolio-gallery\/assets\/images\/admin_images\/divider.line.png') center bottom repeat-x;\r\n}\r\n\r\n.portelement_2 div.right-block > div:last-child {\r\n    background:none;\r\n}\r\n\r\n.portelement_2 div.right-block .title-block_2  {\r\n    margin-top:3px;\r\n}\r\n\r\n.portelement_2 div.right-block .title-block_2 h3 {\r\n    margin:0px;\r\n    padding:0px;\r\n    font-weight:normal;\r\n    font-size:18px !important;\r\n    line-height:22px !important;\r\n    color:#0074a2;\r\n}\r\n\r\n.portelement_2 div.right-block .description-block_2 p,.portelement_2 div.right-block .description-block_2  {\r\n    margin:0px;\r\n    padding:0px;\r\n    font-size:14px;\r\n    color:#555555;\r\n    text-align: justify;\r\n}\r\n\r\n\r\n.portelement_2 div.right-block .description-block_2 h1,\r\n.portelement_2 div.right-block .description-block_2 h2,\r\n.portelement_2 div.right-block .description-block_2 h3,\r\n.portelement_2 div.right-block .description-block_2 h4,\r\n.portelement_2 div.right-block .description-block_2 h5,\r\n.portelement_2 div.right-block .description-block_2 h6,\r\n.portelement_2 div.right-block .description-block_2 p,\r\n.portelement_2 div.right-block .description-block_2 strong,\r\n.portelement_2 div.right-block .description-block_2 span {\r\n    padding:2px !important;\r\n    margin:0px !important;\r\n}\r\n\r\n.portelement_2 div.right-block .description-block_2 ul,\r\n.portelement_2 div.right-block .description-block_2 li {\r\n    padding:2px 0px 2px 5px;\r\n    margin:0px 0px 0px 8px;\r\n}\r\n\r\n.portelement_2 .button-block {\r\n    position:relative;\r\n}\r\n\r\n.portelement_2 div.right-block .button-block a,.portelement_2 div.right-block .button-block a:link,.portelement_2 div.right-block .button-block a:visited {\r\n    position:relative;\r\n    display:inline-block;\r\n    padding:6px 12px;\r\n    background:#2ea2cd;\r\n    color:#ffffff;\r\n    font-size:14px;\r\n    text-decoration:none;\r\n    border:none;\r\n}\r\n\r\n.portelement_2 div.right-block .button-block a:hover,.pupup-elemen.element div.right-block .button-block a:focus,.portelement_2 div.right-block .button-block a:active {\r\n    background:#0074a2;\r\n    color:#ffffff;\r\n    border:none;\r\n}\r\n\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_options_2 {\r\n    position: relative;\r\n    overflow: hidden;\r\nfloat:;margin-top:5px;max-width:180px;width:20%;display:inline-block;margin-left:1%;\r\nopacity: 0;    margin-bottom: 10px;\r\n}\r\n\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_options_2 ul {\r\n    margin: 0px !important;\r\n    padding: 0px !important;\r\n    list-style: none;\r\n}\r\n\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_filters_2 ul {\r\n    margin: 0px !important;\r\n    padding: 0px !important;\r\n    overflow: hidden;\r\n    width: 100%;\r\n}\r\n\r\n\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_options_2 ul li {\r\n    border-radius: 0px;\r\n    list-style-type: none;\r\n    margin: 0px !important;\r\n    padding: 0;\r\nborder: 1px solid #ccc;}\r\n\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_options_2 ul li a {\r\n    background-color: #F7F7F7 !important;\r\n    border-radius: 0px;\r\n    font-size:14px !important;\r\n    color:#555555 !important;\r\n    text-decoration: none;\r\n    cursor: pointer;\r\n    margin: 0px !important;\r\n    display: block;\r\n    padding:3px;\r\n}\r\n\r\n\/*#huge_it_portfolio_content_2 #huge_it_portfolio_options_2 ul li:hover {\r\n\r\n}*\/\r\n\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_options_2 ul li a:hover {\r\n    background-color: #FF3845 !important;\r\n    color:#ffffff !important;\r\n    cursor: pointer;\r\n}\r\n\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_filters_2 {\r\n    position: relative;\r\n    overflow: hidden;\r\nfloat:;margin-top:5px;max-width:180px;width:20%;display:inline-block;margin-left:1%;\r\nopacity: 0;}\r\n\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_filters_2 ul li {\r\n    border-radius: 0px;\r\n    list-style-type: none;\r\nborder: 1px solid #ccc;}\r\n\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_filters_2 ul li a {\r\n    font-size:14px !important;\r\n    color:#555555 !important;\r\n    background-color: #F7F7F7 !important;\r\n    border-radius: 0px;\r\n    padding:3px;\r\n    display: block;\r\n    text-decoration: none;\r\n}\r\n\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_filters_2  ul li a:hover {\r\n    color:#ffffff !important;\r\n    background-color: #FF3845 !important;\r\n    cursor: pointer;\r\n}\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_filters_2 ul li.active a,\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_filters_2 ul li.active a:link,\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_filters_2 ul li.active a:visited,\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_filters_2  ul li.active a:hover,\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_filters_2  ul li.active a:focus,\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_filters_2  ul li.active a:active {\r\n    color:#ffffff !important;\r\n    background-color: #FF3845 !important;\r\n    cursor: pointer;\r\n}\r\n#huge_it_portfolio_content_2 section {\r\n    position:relative;\r\n    display:block;\r\n}\r\n\r\n#huge_it_portfolio_content_2 #huge_it_portfolio_container_2 {\r\n\r\n    width: 79%;\r\nwidth:100%;opacity: 0;\r\n}\r\n@media screen and (max-width: 768px) {\r\n\r\n    #huge_it_portfolio_content_2 #huge_it_portfolio_filters_2 ul li a,\r\n    #huge_it_portfolio_content_2 #huge_it_portfolio_filters_2 ul li a:link,\r\n    #huge_it_portfolio_content_2 #huge_it_portfolio_filters_2 ul li a:visited {\r\n        font-size: 2vw !important;\r\n    }\r\n    #huge_it_portfolio_content_2 #huge_it_portfolio_options_2 ul li a {\r\n        font-size:2vw !important;\r\n    }\r\n\r\n}\r\n\r\n@media screen and (max-width: 600px) {\r\n    .portelement_2 div.left-block_2 {\r\n        width: 100%;\r\n    }\r\n    .portelement_2 div.left-block_2 .main-image-block_2{\r\n        float: left;\r\n    }\r\n    .portelement_2 div.left-block_2 .thumbs-block{\r\n        width: calc(100% - 250px);\r\n        margin-left: 10px;\r\n    }\r\n    .portelement_2 div.left-block_2 .thumbs-block ul{\r\n        width: auto;\r\n    }\r\n    .portelement_2 div.right-block {\r\n        width: 100%;\r\n    }\r\n    .portelement_2 div.right-block .title-block_2 h3 {\r\n        text-align: center;\r\n    }\r\n}\r\n\r\n@media screen and (max-width: 480px) {\r\n\r\n    #huge_it_portfolio_content_2 #huge_it_portfolio_filters_2 ul li a,\r\n    #huge_it_portfolio_content_2 #huge_it_portfolio_filters_2 ul li a:link,\r\n    #huge_it_portfolio_content_2 #huge_it_portfolio_filters_2 ul li a:visited {\r\n        font-size: 3vw !important;\r\n    }\r\n    #huge_it_portfolio_content_2 #huge_it_portfolio_options_2 ul li a {\r\n        line-height: 3vw;\r\n        font-size:3vw !important;\r\n    }\r\n    .portelement_2 div.left-block_2 .thumbs-block{\r\n        width: auto;\r\n    }\r\n    .portelement_2 div.left-block_2 .main-image-block_2  {\r\n        left: 50%;\r\n        transform: translateX(-50%);\r\n        position: relative;\r\n    }\r\n    .portelement_2 div.left-block_2 .thumbs-block ul{\r\n        left: 50%;\r\n        transform: translateX(-50%);\r\n        position: relative;\r\n    }\r\n    .portelement_2 div.left-block_2 .thumbs-block{\r\n        width: 100%;\r\n        margin-left: 0;\r\n    }\r\n}\r\n@media screen and (max-width: 420px) {\r\n\r\n    #huge_it_portfolio_content_2 #huge_it_portfolio_filters_2 ul li a,\r\n    #huge_it_portfolio_content_2 #huge_it_portfolio_filters_2 ul li a:link,\r\n    #huge_it_portfolio_content_2 #huge_it_portfolio_filters_2 ul li a:visited {\r\n        font-size: 4vw !important;\r\n    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