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UBERT: A Novel Language Model for Synonymy Prediction at Scale in the UMLS Metathesaurus ...
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"Is depression related to cannabis?": A Knowledge-infused Model for Entity and Relation Extraction with Limited Supervision
In: Publications (2021)
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3
Analyzing and Learning the Language for Different Types of Harassment
In: Publications (2020)
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ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter
In: Publications (2020)
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5
Analyzing and Learning the Language for Different Types of Harassment
In: Amit P. Sheth (2020)
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ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter
In: Amit P. Sheth (2020)
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7
Personalized Prediction of Suicide Risk for Web-based Intervention
In: Krishnaprasad Thirunarayan (2019)
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8
Personalized Prediction of Suicide Risk for Web-based Intervention
In: Amit P. Sheth (2019)
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9
Personalized Prediction of Suicide Risk for Web-based Intervention
In: Kno.e.sis Publications (2018)
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10
Database and Expert Systems Applications - 28th International Conference, DEXA 2017, Lyon, France, Proceedings part II
Benslimane, Djamal; Damiani, Ernesto; Grosky, William I.. - : HAL CCSD, 2017. : Springer, 2017
In: ISSN: 0302-9743 ; Lecture Notes in Computer Science ; 28th International Conference on Database and Expert Systems Applications and Workshops (DEXA 2017) ; https://hal.archives-ouvertes.fr/hal-03120290 ; Benslimane, Djamal; Damiani, Ernesto; Grosky, William I.; Hameurlain, Abdelkader; Sheth, Amit P.; Wagner, Roland R. 28th International Conference on Database and Expert Systems Applications and Workshops (DEXA 2017), Aug 2017, Lyon, France. Lecture Notes in Computer Science, 10439 (Part II), Springer, 2017, Database and Expert Systems Applications 28th International Conference, DEXA 2017, Lyon, France, 978-3-319-64470-7. ⟨10.1007/978-3-319-64471-4⟩ ; https://link.springer.com/book/10.1007%2F978-3-319-64471-4 (2017)
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RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem
In: Publications (2017)
Abstract: For non-expert users, a textual query is the most popular and simple means for communicating with a retrieval or question answering system. However, there is a risk of receiving queries which do not match with the background knowledge. Query expansion and query rewriting are solutions for this problem but they are in danger of potentially yielding a large number of irrelevant words, which in turn negatively influences runtime as well as accuracy. In this paper, we propose a new method for automatic rewriting input queries on graph-structured RDF knowledge bases. We employ a Hidden Markov Model to determine the most suitable derived words from linguistic resources. We introduce the concept of triple-based co-occurrence for recognizing co-occurred words in RDF data. This model was bootstrapped with three statistical distributions. Our experimental study demonstrates the superiority of the proposed approach to the traditional n-gram model.
Keyword: Computer Engineering; Electrical and Computer Engineering; Hidden Markov Model; Query expansion; query rewriting; RDF
URL: https://corescholar.libraries.wright.edu/knoesis/1119
https://scholarcommons.sc.edu/aii_fac_pub/60
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12
RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem
In: Kno.e.sis Publications (2017)
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13
What Kind of #Communication is Twitter? A Psycholinguistic Perspective on Communication in Twitter for the Purpose of Emergency Coordination
In: Valerie Shalin (2017)
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14
What Kind of #Conversation is Twitter? Mining #Psycholinguistic Cues for Emergency Coordination
In: Valerie Shalin (2017)
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15
RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem
In: Amit P. Sheth (2017)
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16
Intent Classification of Short-Text on Social Media
In: Valerie Shalin (2017)
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17
Context-Aware Semantic Association Ranking
In: Amit P. Sheth (2016)
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18
ezDI's Semantics-Enhanced Linguistic, NLP, and ML Approach for Health Informatics
In: Amit P. Sheth (2016)
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19
Intent Classification of Short-Text on Social Media
In: Amit P. Sheth (2016)
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Intent Classification of Short-Text on Social Media
In: Krishnaprasad Thirunarayan (2016)
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