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	This ontology document is licensed under the Creative Commons
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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=named+entity">
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
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  <description><![CDATA[UMBC ebiquity RSS Tag Search for named entity]]></description>
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    <rdf:Seq>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/461/Extracting-cybersecurity-related-entities-terms-and-concepts-from-text"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/451/Information-Extraction-of-Security-related-entities-and-concepts-from-unstructured-text"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/369/Machine-aided-human-translation-An-automated-system-for-transcribing-dictated-document-translations"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/346/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/337/Collecting-user-annotations-using-Amazon-Mechanical-Turk-and-CrowdFlower"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1022/CyberEnt-A-Cybersecurity-Domain-Specific-Dataset-for-Named-Entity-Recognition"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/965/A-Comparative-Study-of-Deep-Learning-based-Named-Entity-Recognition-Algorithms-for-Cybersecurity"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/872/Creating-Cybersecurity-Knowledge-Graphs-from-Malware-After-Action-Reports"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/885/Gazetteer-Generation-for-Neural-Named-Entity-Recognition"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/884/Improving-Neural-Named-Entity-Recognition-with-Gazetteers"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/867/Named-Entity-Recognition-for-Nepali-Language"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/730/HLTCOE-Participation-in-TAC-KBP-2015-Cold-Start-and-TEDL"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/626/Information-Extraction-of-Security-related-entities-and-concepts-from-unstructured-text-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/499/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/476/Annotating-named-entities-in-Twitter-data-with-crowdsourcing"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/355/Annotations-of-Cybersecurity-blogs-and-articles"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/461/Extracting-cybersecurity-related-entities-terms-and-concepts-from-text">
  <title><![CDATA[Extracting cybersecurity related entities, terms and concepts from text]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/461/Extracting-cybersecurity-related-entities-terms-and-concepts-from-text</link>
  <description><![CDATA[Securing computers, data, cyber-physical systems and networks is a growing problem as society&#39;s dependence on them increases while they remain vulnerable to attacks by both criminals and rival nation states. Creating &#39;situationally aware&#39; computer systems that defend against new &quot;zero day&quot; software vulnerabilities requires them to automatically integrate and use new security-related data from a wide variety of sources. One important source is information found in text fr...]]></description>
  <dc:date>2013-05-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/451/Information-Extraction-of-Security-related-entities-and-concepts-from-unstructured-text">
  <title><![CDATA[Information Extraction of Security related entities and concepts from unstructured text]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/451/Information-Extraction-of-Security-related-entities-and-concepts-from-unstructured-text</link>
  <description><![CDATA[Cyber Security has been a big concern especially in past one decade where it is witnessed that targets ranging from large number of internet users to government agencies are being attacked because of vulnerabilities present in the system. Even though these vulnerabilities are identified and published publicly but response has always been slow in covering up these vulnerabilities because there is no automatic mechanism to understand and process this unstructured text that is published on inter...]]></description>
  <dc:date>2013-04-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/369/Machine-aided-human-translation-An-automated-system-for-transcribing-dictated-document-translations">
  <title><![CDATA[Machine aided human translation - An automated system for transcribing dictated document translations]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/369/Machine-aided-human-translation-An-automated-system-for-transcribing-dictated-document-translations</link>
  <description><![CDATA[A model is presented for machine aided human translation (MAHT) that integrates source language text and target language acoustic information to produce the text translation of source language document. It is evaluated on a scenario where a human translator dictates a first draft target language translation of a source language document. Information obtained from the source language document, including translation probabilities derived from statistical machine translation (SMT) and named enti...]]></description>
  <dc:date>2010-10-08</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/346/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data">
  <title><![CDATA[Improving Accuracy of Named Entity Recognition on Social Media Data]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/346/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data</link>
  <description><![CDATA[Master's Thesis Defense

In recent years, social media outlets such as Twitter and Facebook have drawn attention from companies and researchers interested in detecting trends.  The informal nature of status updates from these services leads to a higher volume of updates, because each update takes little care to generate, but each update is usually short and noisy (misspellings, lack of punctuation, non-standard abbreviations and capitalization).  These shortcomings cause traditional Natural...]]></description>
  <dc:date>2010-05-19</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/337/Collecting-user-annotations-using-Amazon-Mechanical-Turk-and-CrowdFlower">
  <title><![CDATA[Collecting user annotations using Amazon Mechanical Turk and CrowdFlower]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/337/Collecting-user-annotations-using-Amazon-Mechanical-Turk-and-CrowdFlower</link>
  <description><![CDATA[Will Murnane and Anand Karandikar will talk about using Amazon Mechanical Turk and CrowdFlower systems to collect user annotations for NER task on Twitter statuses. These annotations will help towards developing better named entity recognizers for domains such as Twitter and Facebook.]]></description>
  <dc:date>2010-03-09</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1022/CyberEnt-A-Cybersecurity-Domain-Specific-Dataset-for-Named-Entity-Recognition">
  <title><![CDATA[CyberEnt: A Cybersecurity Domain Specific Dataset for Named Entity Recognition]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1022/CyberEnt-A-Cybersecurity-Domain-Specific-Dataset-for-Named-Entity-Recognition</link>
  <description><![CDATA[Named Entity Recognition (NER) is a critical component of automated knowledge extraction. It allows Natural Language Processing (NLP) models to label instances of real-world entities that are important in the context of the text. To be able to accomplish this, the NLP model needs to be trained on large corpora of human-annotated text. There are examples of general, domain-agonistic text corpora available, but they are not suited for fields such as cybersecurity, that require domain-specific t...]]></description>
  <dc:date>2022-04-18</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/965/A-Comparative-Study-of-Deep-Learning-based-Named-Entity-Recognition-Algorithms-for-Cybersecurity">
  <title><![CDATA[A Comparative Study of Deep Learning based Named Entity Recognition Algorithms for Cybersecurity]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/965/A-Comparative-Study-of-Deep-Learning-based-Named-Entity-Recognition-Algorithms-for-Cybersecurity</link>
  <description><![CDATA[Named Entity Recognition (NER) is important in the cybersecurity domain. It helps researchers extract cyber threat information from unstructured text sources. The extracted cyber entities or key expressions can be used to model a cyber-attack described in an open-source text. A large number of general-purpose NER algorithms have been published that work well in text analysis. These algorithms do not perform well when applied to the cybersecurity domain. In the field of cybersecurity, the open...]]></description>
  <dc:date>2020-12-10</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/872/Creating-Cybersecurity-Knowledge-Graphs-from-Malware-After-Action-Reports">
  <title><![CDATA[Creating Cybersecurity Knowledge Graphs from Malware After Action Reports]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/872/Creating-Cybersecurity-Knowledge-Graphs-from-Malware-After-Action-Reports</link>
  <description><![CDATA[After Action Reports (AARs) provide incisive analysis of cyber-incidents. Extracting cyber-knowledge from these sources would provide security analysts with credible information, which they can use to detect or find patterns indicative of a cyber-attack. In this paper, we describe a system to extract information from AARs, aggregate the extracted information by fusing similar entities together, and represent that extracted information in a Cybersecurity Knowledge Graph (CKG). We extract entit...]]></description>
  <dc:date>2020-12-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/885/Gazetteer-Generation-for-Neural-Named-Entity-Recognition">
  <title><![CDATA[Gazetteer Generation for Neural Named Entity Recognition]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/885/Gazetteer-Generation-for-Neural-Named-Entity-Recognition</link>
  <description><![CDATA[We present a way to generate gazetteers from the Wikidata knowledge graph and use the lists to improve a neural NER system by adding an input feature indicating that a word is part of a name in the gazetteer.  We empirically show that the approach yields performance gains in two distinct languages: a high-resource, word-based language, English, and a high-resource, character-based language, Chinese.  We apply the approach to a low-resource language, Russian, using a new annotated Russian NER ...]]></description>
  <dc:date>2020-05-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/884/Improving-Neural-Named-Entity-Recognition-with-Gazetteers">
  <title><![CDATA[Improving Neural Named Entity Recognition with Gazetteers]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/884/Improving-Neural-Named-Entity-Recognition-with-Gazetteers</link>
  <description><![CDATA[The goal of this work is to improve the performance of a neural named entity recognition system by adding input features that indicate a word is part of a name included in a gazetteer. This article describes how to generate gazetteers from the Wikidata knowledge graph as well as how to integrate the information into a neural NER system. Experiments reveal that the approach yields performance gains in two distinct languages: a high-resource, word-based language, English, and a high-resource, c...]]></description>
  <dc:date>2020-03-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/867/Named-Entity-Recognition-for-Nepali-Language">
  <title><![CDATA[Named Entity Recognition for Nepali Language]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/867/Named-Entity-Recognition-for-Nepali-Language</link>
  <description><![CDATA[Named Entity Recognition have been studied for different languages like English, German, Spanish and many others but no study have focused on Nepali language. In this paper we propose a neural based Nepali NER using latest state-of-the-art architecture based on grapheme-level which doesn't require any hand-crafted features and no data pre-processing. Our novel neural based model gained relative improvement of 33% to 50% compared to feature based SVM model and up to 10% improvement over state-...]]></description>
  <dc:date>2019-08-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/730/HLTCOE-Participation-in-TAC-KBP-2015-Cold-Start-and-TEDL">
  <title><![CDATA[HLTCOE Participation in TAC KBP 2015: Cold Start and TEDL]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/730/HLTCOE-Participation-in-TAC-KBP-2015-Cold-Start-and-TEDL</link>
  <description><![CDATA[The JHU HLTCOE participated in the Cold Start and the Trilingual Entity Linking and Discovery tasks of the 2015 Text Analysis Conference Knowledge Base Population evaluation. For our fourth year of participation in Cold Start we continued our research with the KELVIN system. We submitted experimental variants that explore use of linking to Freebase and adding additional relations.  This is our first year of participation in EDL. We used KELVIN in three runs and experimented with an alternate ...]]></description>
  <dc:date>2015-11-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/626/Information-Extraction-of-Security-related-entities-and-concepts-from-unstructured-text-">
  <title><![CDATA[Information Extraction of Security related entities and concepts from unstructured text.]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/626/Information-Extraction-of-Security-related-entities-and-concepts-from-unstructured-text-</link>
  <description><![CDATA[Cyber Security has been a big concern especially in past one decade where it is witnessed
that targets ranging from large number of internet users to government agencies are
being attacked because of vulnerabilities present in the system. Even though these vulnerabilities
are identified and published publicly but response has always been slow in covering
up these vulnerabilities because there is no automatic mechanism to understand and process
this unstructured text that is published on ...]]></description>
  <dc:date>2013-05-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/499/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data">
  <title><![CDATA[Improving Accuracy of Named Entity Recognition on Social Media Data]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/499/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data</link>
  <description><![CDATA[In recent years, social media outlets such as Twitter and Facebook have drawn attention from companies and researchers interested in detecting trends. The informal nature of status updates from these services leads to a higher volume of updates, because each update takes little care to generate, but each update is usually short and noisy (misspellings, lack of punctuation, non-standard abbreviations and capitalization). These shortcomings cause traditional Natural Language Processing (NLP) te...]]></description>
  <dc:date>2010-08-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/476/Annotating-named-entities-in-Twitter-data-with-crowdsourcing">
  <title><![CDATA[Annotating named entities in Twitter data with crowdsourcing]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/476/Annotating-named-entities-in-Twitter-data-with-crowdsourcing</link>
  <description><![CDATA[We describe our experience using both Amazon Mechanical Turk (MTurk) and Crowd Flower to collect simple named entity annotations for Twitter status updates. Unlike most genres that have traditionally been the focus of named entity experiments, Twitter is far more informal and abbreviated. The collected annotations and annotation techniques will provide a first step toward the full study of named entity recognition in domains like Facebook and Twitter. We also briefly describe how to use MTurk...]]></description>
  <dc:date>2010-06-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/355/Annotations-of-Cybersecurity-blogs-and-articles">
  <title><![CDATA[Annotations of Cybersecurity blogs and articles]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/355/Annotations-of-Cybersecurity-blogs-and-articles</link>
  <description><![CDATA[This data is the result of a Master Thesis Project by Ravendar Lal under the supervision of Dr. Tim Finin. This dataset can be used for training technical systems. This dataset consists of manually data for cybersecurity domain where this data collection has the articles from CVES, Adobe Security Bulletins, Microsoft Security Bulletins and various blog posts. Total data has over 45,000 tokens and 5,000 tagged entities. Annotation was done by the Graduate Students of Computer Science Departmen...]]></description>
  <dc:date>2013-05-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/297/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data">
  <title><![CDATA[Improving Accuracy of Named Entity Recognition on Social Media Data]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/297/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data</link>
  <description><![CDATA[We present a system for improving the accuracy of one NLP technique, Named Entity Recognition or NER, on Twitter data by training a recognizer specifically for this type of data.  This training data is obtained from the Amazon Mechanical Turk crowdsourcing platform.]]></description>
  <dc:date>2010-05-08</dc:date>
 </item>
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