{"id":714655,"date":"2020-12-30T02:54:42","date_gmt":"2020-12-30T10:54:42","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=714655"},"modified":"2021-03-24T19:03:09","modified_gmt":"2021-03-25T02:03:09","slug":"mention-and-entity-description-co-attention-for-entity-disambiguation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mention-and-entity-description-co-attention-for-entity-disambiguation\/","title":{"rendered":"Mention and Entity Description Co-Attention for Entity Disambiguation"},"content":{"rendered":"<div>For the task of entity disambiguation, mention contexts and entity descriptions both contain various kinds of information content while only a subset of them are helpful for disambiguation. In this paper, we propose a type-aware co-attention model for entity disambiguation, which tries to identify the most discriminative words from mention contexts and most relevant sentences from corresponding entity descriptions simultaneously. To bridge the semantic gap between mention contexts and entity descriptions, we further incorporate entity type information to enhance the co-attention mechanism. Our evaluation shows that the proposed model outperforms the state-of-the-arts on three public datasets. Further analysis also confirms that both the co-attention mechanism and the type-aware mechanism are effective.<\/div>\n","protected":false},"excerpt":{"rendered":"<p>For the task of entity disambiguation, mention contexts and entity descriptions both contain various kinds of information content while only a subset of them are helpful for disambiguation. In this paper, we propose a type-aware co-attention model for entity disambiguation, which tries to identify the most discriminative words from mention contexts and most relevant sentences [&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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13545,13555],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[246694,246691,246808],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-714655","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-language-technologies","msr-research-area-search-information-retrieval","msr-locale-en_us","msr-field-of-study-artificial-intelligence","msr-field-of-study-computer-science","msr-field-of-study-natural-language-processing"],"msr_publishername":"Association for the Advancement of Artificial Intelligence","msr_edition":"","msr_affiliation":"","msr_published_date":"2018-1-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"Association for the Advancement of Artificial Intelligence","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/dblp.uni-trier.de\/db\/conf\/aaai\/aaai2018.html#NieCWLP18","label_id":"243109","label":0},{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI18\/paper\/viewFile\/16382\/16156","label_id":"243132","label":0},{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI18\/paper\/view\/16382","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Feng Nie","user_id":0,"rest_url":false},{"type":"text","value":"Yunbo Cao","user_id":0,"rest_url":false},{"type":"text","value":"Jinpeng Wang","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Chin-Yew Lin","user_id":31493,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Chin-Yew Lin"},{"type":"text","value":"Rong Pan","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[199560],"msr_event":[],"msr_group":[144919],"msr_project":[717721,714646],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":717721,"post_title":"ezPitch: Connecting Salespersons and Customers through Relevant News","post_name":"ezpitch-connecting-salespersons-and-customers-through-relevant-news","post_type":"msr-project","post_date":"2021-01-19 08:25:06","post_modified":"2021-01-19 08:28:44","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/ezpitch-connecting-salespersons-and-customers-through-relevant-news\/","post_excerpt":"The goal of ezPitch is connecting salespersons and customers through relevant news. Why is this important? In the daily work, the sales persons need to search, track and explore the related news about customers before talking to them. For example, if there is management change in the customer\u2019s company. The sales person may need to find a way to re-build the relationship with the new leadership. If the customer\u2019s company announces an earnings report which&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/717721"}]}},{"ID":714646,"post_title":"VERT: Versatile Entity Recognition &amp; Disambiguation Toolkit","post_name":"vert-versatile-entity-recognition-disambiguation-toolkit","post_type":"msr-project","post_date":"2020-12-30 02:54:35","post_modified":"2021-10-13 21:15:01","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/vert-versatile-entity-recognition-disambiguation-toolkit\/","post_excerpt":"While knowledge about entities is a key building block in the mentioned systems, creating effective\/efficient models for real-world scenarios remains a challenge (tech\/data\/real workloads). Based on such needs, we've created VERT - a Versatile Entity Recognition &amp; Disambiguation Toolkit. VERT is a pragmatic toolkit that combines rules and ML, offering both powerful pretrained models for core entity types (recognition and linking) and the easy creation of custom models. 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