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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=word+embedding">
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  <description><![CDATA[UMBC ebiquity RSS Tag Search for word embedding]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1146/Employing-Word-Embedding-for-Schema-Matching-in-Standard-Lifecycle-Management"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/963/A-Semantically-Rich-Framework-for-Knowledge-Representation-of-Code-of-Federal-Regulations-CFR-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/957/Fine-and-Ultra-Fine-Entity-Type-Embeddings-for-Question-Answering"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/874/CASIE-Extracting-Cybersecurity-Event-Information-from-Text"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/877/Two-Tier-Analysis-of-Social-Media-Collaboration-for-Student-Migration"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/967/Query-Expansion-for-Cross-Language-Question-Re-Ranking"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/777/A-Deep-Learning-Approach-to-Understanding-Cloud-Service-Level-Agreements-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/719/Robust-Semantic-Text-Similarity-Using-LSA-Machine-Learning-and-Linguistic-Resources"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/379/Cybersecurity-embeddings"/>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1146/Employing-Word-Embedding-for-Schema-Matching-in-Standard-Lifecycle-Management">
  <title><![CDATA[Employing Word-Embedding for Schema Matching in Standard Lifecycle Management]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1146/Employing-Word-Embedding-for-Schema-Matching-in-Standard-Lifecycle-Management</link>
  <description><![CDATA[Today, businesses rely on numerous information systems to achieve their production goals and improve their global competitiveness. Semantically integrating those systems is essential for businesses to achieve both. To do so, businesses must rely on standards, the most important of which are data exchange standards (DES). DES focus on technical and business semantics that are needed to deliver quality and timely products and services. Consequently, the ability for businesses to quickly use and...]]></description>
  <dc:date>2024-03-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/963/A-Semantically-Rich-Framework-for-Knowledge-Representation-of-Code-of-Federal-Regulations-CFR-">
  <title><![CDATA[A Semantically Rich Framework for Knowledge Representation of Code of Federal Regulations (CFR)]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/963/A-Semantically-Rich-Framework-for-Knowledge-Representation-of-Code-of-Federal-Regulations-CFR-</link>
  <description><![CDATA[Federal government agencies and organizations doing business with them have to adhere to the Code of Federal Regulations (CFR). The CFRs are currently available as large text documents that are not machine-processable and so require extensive manual effort to parse and comprehend, especially when sections cross-reference topics spread across various titles. We have developed a novel framework to automatically extract knowledge from CFRs and represent it using a semantically rich knowledgegrap...]]></description>
  <dc:date>2020-12-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/957/Fine-and-Ultra-Fine-Entity-Type-Embeddings-for-Question-Answering">
  <title><![CDATA[Fine and Ultra-Fine Entity Type Embeddings  for Question Answering]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/957/Fine-and-Ultra-Fine-Entity-Type-Embeddings-for-Question-Answering</link>
  <description><![CDATA[We describe our system for the SeMantic AnsweR (SMART) Type prediction task 2020 for both the DBpedia and Wikidata Question Answer Type datasets. The SMART task challenge introduced fine-grained and ultra-fine entity typing to question answering by releasing two datasets for question classification using DBpedia and Wikidata classes. We propose a flexible framework for both entity types using paragraph vectors and word embeddings to obtain high-quality contextualized question representations....]]></description>
  <dc:date>2020-11-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/874/CASIE-Extracting-Cybersecurity-Event-Information-from-Text">
  <title><![CDATA[CASIE: Extracting Cybersecurity Event Information from Text]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/874/CASIE-Extracting-Cybersecurity-Event-Information-from-Text</link>
  <description><![CDATA[We present CASIE, a system that extracts information about cybersecurity events from text and populates a semantic model, with the ultimate goal of integration into a knowledge graph of cybersecurity data. It was trained on a new corpus of 1,000 English news articles from 2017–2019 labeled with rich, event-based annotations that cover both cyberattack and vulnerability-related events. Our model defines five event subtypes along with their semantic roles and 20 event-relevant argument types ...]]></description>
  <dc:date>2020-02-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/877/Two-Tier-Analysis-of-Social-Media-Collaboration-for-Student-Migration">
  <title><![CDATA[Two Tier Analysis of Social Media Collaboration for Student Migration]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/877/Two-Tier-Analysis-of-Social-Media-Collaboration-for-Student-Migration</link>
  <description><![CDATA[Global adoption of Social Media as the preferred medium for collaboration and information exchange is increasingly reshaping social realities and facilitating new research methodologies in various disciplines. Social Media applications are collecting a large amount of User-Generated Content (UGC) and web data that contains knowledge about novel approaches of global collaboration between people. We have done a detailed study of the factors that lead to student migration, as espoused by social ...]]></description>
  <dc:date>2019-12-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/967/Query-Expansion-for-Cross-Language-Question-Re-Ranking">
  <title><![CDATA[Query Expansion for Cross-Language Question Re-Ranking]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/967/Query-Expansion-for-Cross-Language-Question-Re-Ranking</link>
  <description><![CDATA[Community question-answering (CQA) platforms have become very popular forums for asking and answering questions daily.  While these forums are rich repositories of community knowledge, they present challenges for finding relevant answers and similar questions, due to the open-ended nature of informal discussions.  Further, if the platform allows questions and answers in multiple languages, we are faced with the additional challenge of matching cross-lingual information. In this work, we focus...]]></description>
  <dc:date>2019-04-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/777/A-Deep-Learning-Approach-to-Understanding-Cloud-Service-Level-Agreements-">
  <title><![CDATA[A Deep Learning Approach to Understanding Cloud Service Level Agreements]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/777/A-Deep-Learning-Approach-to-Understanding-Cloud-Service-Level-Agreements-</link>
  <description><![CDATA[Educational organizations, like Universities and School Systems, are rapidly adopting Cloud based services to provide Information Technology (IT) infrastructure to their students. These include course offerings, class materials, ​data storage, emailing and collaboration software, virtual computing environment, etc. Moreover, cloud providers, like Amazon, are also providing free computing credits targeted to students. The legal documents associated with cloud based
services, such as Service...]]></description>
  <dc:date>2017-05-24</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/719/Robust-Semantic-Text-Similarity-Using-LSA-Machine-Learning-and-Linguistic-Resources">
  <title><![CDATA[Robust Semantic Text Similarity Using LSA, Machine Learning and Linguistic Resources]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/719/Robust-Semantic-Text-Similarity-Using-LSA-Machine-Learning-and-Linguistic-Resources</link>
  <description><![CDATA[Semantic textual similarity is a measure of the degree of semantic equivalence between two pieces of text. We describe the SemSim system and its performance in the *SEM~2013~and SemEval-2014~tasks on semantic textual similarity. At the core of our system lies a robust distributional word similarity component that combines Latent Semantic Analysis and machine learning augmented with data from several linguistic resources. We used a simple term alignment algorithm to handle longer pieces of tex...]]></description>
  <dc:date>2016-03-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/379/Cybersecurity-embeddings">
  <title><![CDATA[Cybersecurity embeddings]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/379/Cybersecurity-embeddings</link>
  <description><![CDATA[This is a word embedding model produced by Dr. Youngja Park of IBM Research using word2vec applied to a collection of one million documents found on the Web relevant to cybersecurity.  Tokenization was done using whitespace, resulting in 917,213,530 tokens of which 6,417,554 were unique. The word2 vec model has 100 dimensions and a vocabulary of 1,013,092 terms.  If you use this model in your research, please cite this document to refer to the model.

Ankur Padia, Arpita Roy, Taneeya Satyap...]]></description>
  <dc:date>2018-04-01</dc:date>
 </item>
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