21 |
Sentiment Big Data Flow Analysis by Means of Dynamic Linguistic Patterns
|
|
|
|
BASE
|
|
Show details
|
|
22 |
Semantic Annotations for Workflow Interoperability
|
|
|
|
In: ISSN: 0350-5596 ; Informatica ; https://hal.inria.fr/hal-01111453 ; Informatica, Slovene Society Informatika, Ljubljana, 2014, 38 (4), pp.347-366 (2014)
|
|
BASE
|
|
Show details
|
|
23 |
Fuzzy modeling of a composite agronomical feature using FisPro: the case of vine vigor
|
|
|
|
In: 15th International Conference, IPMU 2014 ; https://hal.inrae.fr/hal-02601052 ; 15th International Conference, IPMU 2014, Jul 2014, Montpellier, France. pp.127-137, ⟨10.1007/978-3-319-08795-5_14⟩ (2014)
|
|
BASE
|
|
Show details
|
|
24 |
Natural Language Semantics using Probabilistic Logic
|
|
|
|
In: DTIC (2014)
|
|
BASE
|
|
Show details
|
|
25 |
Distributed Non-Parametric Representations for Vital Filtering: UW at TREC KBA 2014
|
|
|
|
In: DTIC (2014)
|
|
Abstract:
Identifying documents that contain timely and vital information for an entity of interest, a task known as vital filtering, has become increasingly important with the availability of large document collections. To efficiently filter such large text corpora in a streaming manner, we need to compactly represent previously observed entity contexts and quickly estimate whether a new document contains novel information. Existing approaches to modeling contexts, such as bag of words, latent semantic indexing, and topic models are limited in several respects: they are unable to handle streaming data, do not model the underlying topic of each document, suffer from lexical sparsity, and/or do not accurately estimate temporal vitalness. In this paper, we introduce a word embedding-based non-parametric representation of entities that addresses the above limitations. The word embeddings provide accurate and compact summaries of observed entity contexts further described by topic clusters that are estimated in a non-parametric manner. Additionally we associate a staleness measure with each entity and topic cluster, dynamically estimating their temporal relevance. This approach of using word embeddings, non-parametric clustering, and staleness provides an efficient yet appropriate representation of entity contexts for the streaming setting, enabling accurate vital filtering. ; Presented at of the Twenty-Third Text REtrieval Conference (TREC 2014) held in Gaithersburg, Maryland, November 19-21, 2014. The conference was co-sponsored by the National Institute of Standards and Technology (NIST) and the Defense Advanced Research Projects Agency (DARPA).
|
|
Keyword:
*INFORMATION RETRIEVAL; *KNOWLEDGE BASED SYSTEMS; CLASSIFICATION; CLUSTERING; COMPUTATIONAL LINGUISTICS; Cybernetics; DATA MINING; EMBEDDING; FEATURE EXTRACTION; Information Science; INTELLIGENT INFORMATION; INTERNET; LEARNING MACHINES; LEXICOGRAPHY; MATHEMATICAL FILTERS; PATTERN RECOGNITION; SEMANTICS; SEMI-SUPERVISED APPROACH; STREAMING DATA; SYSTEMS ENGINEERING; TEXT PROCESSING; VECTOR ANALYSIS
|
|
URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA618587 http://www.dtic.mil/docs/citations/ADA618587
|
|
BASE
|
|
Hide details
|
|
26 |
A systematic approach towards creative urban design
|
|
|
|
In: In: Gero, J, (ed.) Design Computing and Cognition '12. (pp. 133-150). Springer Netherlands: Netherlands. (2014) (2014)
|
|
BASE
|
|
Show details
|
|
27 |
A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications
|
|
|
|
In: Krishnaprasad Thirunarayan (2014)
|
|
BASE
|
|
Show details
|
|
28 |
Updating Relational Views Using Knowledge at View Definition and View Update Time
|
|
|
|
In: Amit P. Sheth (2014)
|
|
BASE
|
|
Show details
|
|
29 |
A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications
|
|
|
|
In: Amit P. Sheth (2014)
|
|
BASE
|
|
Show details
|
|
30 |
Accelerating Exploitation of Low-grade Intelligence through Semantic Text Processing of Social Media
|
|
|
|
In: DTIC (2013)
|
|
BASE
|
|
Show details
|
|
31 |
A Graph-Based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications
|
|
|
|
In: Kno.e.sis Publications (2013)
|
|
BASE
|
|
Show details
|
|
32 |
Semantic levels of domain-independent commonsense knowledgebase for visual indexing and retrieval applications
|
|
|
|
BASE
|
|
Show details
|
|
33 |
A Framework for Modeling and Simulation of the Artificial
|
|
|
|
In: DTIC (2012)
|
|
BASE
|
|
Show details
|
|
34 |
Capturing Cultural Glossaries: Case-study I *
|
|
|
|
In: Lexikos; Vol. 13 (2003) ; 2224-0039 (2011)
|
|
BASE
|
|
Show details
|
|
35 |
PSYCHONET 2: contextualized and enriched psycholinguistic commonsense ontology
|
|
|
|
BASE
|
|
Show details
|
|
36 |
A Concept Map Knowledge Model of Intelligence Analysis
|
|
|
|
In: DTIC (2011)
|
|
BASE
|
|
Show details
|
|
37 |
Capturing Cultural Glossaries: Case-study I *
|
|
|
|
In: Lexikos, Vol 13 (2011) (2011)
|
|
BASE
|
|
Show details
|
|
38 |
Transfer Learning in Integrated Cognitive Systems
|
|
|
|
In: DTIC (2010)
|
|
BASE
|
|
Show details
|
|
39 |
Validation Workshop of the DRDC Concept Map Knowledge Model: Issues in Intelligence Analysis
|
|
|
|
In: DTIC (2010)
|
|
BASE
|
|
Show details
|
|
40 |
Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models
|
|
|
|
In: DTIC (2009)
|
|
BASE
|
|
Show details
|
|
|
|