{"id":240824,"date":"2009-06-22T20:20:06","date_gmt":"2009-06-23T03:20:06","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=240824"},"modified":"2018-10-16T20:08:58","modified_gmt":"2018-10-17T03:08:58","slug":"statsnowball-statistical-approach-extracting-entity-relationships","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/statsnowball-statistical-approach-extracting-entity-relationships\/","title":{"rendered":"StatSnowball: a Statistical Approach to Extracting Entity Relationships"},"content":{"rendered":"<div id=\"citationdetails\" class=\" x-tabs-top\">\n<div id=\"tab-body9\" class=\" x-tabs-body\">\n<div id=\"abstract\" class=\"ytab x-tabs-item-body\">\n<div class=\"tabbody\">\n<div>\n<p>Traditional relation extraction methods require pre-specified relations and relation-specific human-tagged examples. Bootstrapping systems significantly reduce the number of training examples, but they usually apply heuristic-based methods to combine a set of strict hard rules, which limit the ability to generalize and thus generate a low recall. Furthermore, existing bootstrapping methods do not perform open information extraction (Open IE), which can identify various types of relations without requiring pre-specifications. In this paper, we propose a statistical extraction framework called <i>Statistical Snowball<\/i> (StatSnowball), which is a bootstrapping system and can perform both traditional relation extraction and Open IE.<\/p>\n<p>StatSnowball uses the discriminative Markov logic networks (MLNs) and softens hard rules by learning their weights in a maximum likelihood estimate sense. MLN is a general model, and can be configured to perform different levels of relation extraction. In StatSnwoball, pattern selection is performed by solving an l<sub>1<\/sub>-norm penalized maximum likelihood estimation, which enjoys well-founded theories and efficient solvers. We extensively evaluate the performance of StatSnowball in different configurations on both a small but fully labeled data set and large-scale Web data. Empirical results show that StatSnowball can achieve a significantly higher recall without sacrificing the high precision during iterations with a small number of seeds, and the joint inference of MLN can improve the performance. Finally, StatSnowball is efficient and we have developed a working entity relation search engine called <i>Renlifang<\/i> based on it.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Traditional relation extraction methods require pre-specified relations and relation-specific human-tagged examples. Bootstrapping systems significantly reduce the number of training examples, but they usually apply heuristic-based methods to combine a set of strict hard rules, which limit the ability to generalize and thus generate a low recall. Furthermore, existing bootstrapping methods do not perform open information [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"zhu"},{"type":"user_nicename","value":"znie"},{"type":"user_nicename","value":"xiaojl"},{"type":"user_nicename","value":"zhabo"},{"type":"user_nicename","value":"jrwen"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"WWW 2009","msr_editors":"","msr_how_published":"","msr_isbn":"978-1-60558-487-4","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"WWW 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The Chinese-language version is called Renlifang. The need for collecting and understanding Web information about a real-world entity (such as a person or a product) is mostly collated manually through search engines. However, information about a single entity might appear in thousands of Web pages. 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