Paradigmatic Clustering for NLP
| dc.contributor.author | Santisteban Pablo, Julio Omar | |
| dc.contributor.author | Tejada Cárcamo, Javier | |
| dc.date.accessioned | 2019-01-29T22:19:53Z | |
| dc.date.available | 2019-01-29T22:19:53Z | |
| dc.date.issued | 2016 | |
| dc.description.abstract | How can we retrieve meaningful information from a large and sparse graph?. Traditional approaches focus on generic clustering techniques and discovering dense cumulus in a network graph, however, they tend to omit interesting patterns such as the paradigmatic relations. In this paper, we propose a novel graph clustering technique modelling the relations of a node using the paradigmatic analysis. We exploit node's relations to extract its existing sets of signifiers. The newly found clusters represent a different view of a graph, which provides interesting insights into the structure of a sparse network graph. Our proposed algorithm PaC (Paradigmatic Clustering) for clustering graphs uses paradigmatic analysis supported by a asymmetric similarity, in contrast to traditional graph clustering methods, our algorithm yields worthy results in tasks of word-sense disambiguation. In addition we propose a novel paradigmatic similarity measure. Extensive experiments and empirical analysis are used to evaluate our algorithm on synthetic and real data. © 2015 IEEE. | es_PE |
| dc.description.uri | Trabajo de investigación | es_PE |
| dc.identifier.doi | https://doi.org/10.1109/ICDMW.2015.233 | es_PE |
| dc.identifier.isbn | urn:isbn:9781467384926 | es_PE |
| dc.identifier.uri | https://hdl.handle.net/20.500.12590/15843 | |
| dc.language.iso | eng | es_PE |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | es_PE |
| dc.relation.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964770393&doi=10.1109%2fICDMW.2015.233&partnerID=40&md5=26ff37a5a3402b53a73baf00f81bd862 | es_PE |
| dc.rights | info:eu-repo/semantics/restrictedAccess | es_PE |
| dc.source | Repositorio Institucional - UCSP | es_PE |
| dc.source | Universidad Católica San Pablo | es_PE |
| dc.source | Scopus | es_PE |
| dc.subject | Cluster analysis | es_PE |
| dc.subject | Data mining | es_PE |
| dc.subject | Graph theory | es_PE |
| dc.subject | Natural language processing systems | es_PE |
| dc.subject | asymmetric similarity | es_PE |
| dc.subject | clustering | es_PE |
| dc.subject | Clustering techniques | es_PE |
| dc.subject | paradigmatic | es_PE |
| dc.subject | Similarity measure | es_PE |
| dc.subject | Synthetic and real data | es_PE |
| dc.subject | Traditional approaches | es_PE |
| dc.subject | Word Sense Disambiguation | es_PE |
| dc.subject | Clustering algorithms | es_PE |
| dc.subject.ocde | https://purl.org/pe-repo/ocde/ford#1.02.00 | es_PE |
| dc.title | Paradigmatic Clustering for NLP | es_PE |
| dc.type | info:eu-repo/semantics/conferenceObject |