Soft-constrained inference for Named Entity Recognition (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• Soft-constrained inference for Named Entity Recognition (Articolo in rivista) (literal)

Anno

• 2014-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1016/j.ipm.2014.04.005 (literal)

Alternative label

• Fersini E.; Messina E.; Felici G.; Roth D. (2014)
  Soft-constrained inference for Named Entity Recognition
  in Information processing & management
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Fersini E.; Messina E.; Felici G.; Roth D. (literal)

Pagina inizio

• 807 (literal)

Pagina fine

• 819 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

• http://www.scopus.com/inward/record.url?eid=2-s2.0-84904385510&partnerID=q2rCbXpz (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 50 (literal)

Rivista

• Information processing & management (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

• 5 (literal)

Note

• Scopu (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• DISCo, University of Milano - Bicocca, Viale Sarca, 336, 20126 Milano, Italy; CNR, Institute for Systems Analysis and Computer Science, Viale Manzoni, 30, 00185 Roma, Italy; Department of Computer Science, University of Illinois at Urbana-Champaign, 2700 Prairie Meadow Dr., Champaign, IL 61822, United States (literal)

Titolo

• Soft-constrained inference for Named Entity Recognition (literal)

Abstract

• Much of the valuable information in supporting decision making processes originates in text-based documents. Although these documents can be effectively searched and ranked by modern search engines, actionable knowledge need to be extracted and transformed in a structured form before being used in a decision process. In this paper we describe how the discovery of semantic information embedded in natural language documents can be viewed as an optimization problem aimed at assigning a sequence of labels (hidden states) to a set of interdependent variables (textual tokens). Dependencies among variables are efficiently modeled through Conditional Random Fields, an indirected graphical model able to represent the distribution of labels given a set of observations. The Markov property of these models prevent them to take into account long-range dependencies among variables, which are indeed relevant in Natural Language Processing. In order to overcome this limitation we propose an inference method based on Integer Programming formulation of the problem, where long distance dependencies are included through non-deterministic soft constraints. © 2014 Elsevier Ltd. All rights reserved. (literal)

Prodotto di

• Data Mining, Identificazione e Stima per Sistemi Complessi (ICT.P11.004.001) (Modulo)
• Istituto di analisi dei sistemi ed informatica \"Antonio Ruberti\" (IASI) (Istituto)

Autore CNR

• GIOVANNI FELICI (Unità di personale interno)

Insieme di parole chiave

• Keywords of "Soft-constrained inference for Named Entity Recognition" (Insieme di parole chiave)

Incoming links:

Autore CNR di

• GIOVANNI FELICI (Unità di personale interno)

Prodotto

• Istituto di analisi dei sistemi ed informatica \"Antonio Ruberti\" (IASI) (Istituto)
• Data Mining, Identificazione e Stima per Sistemi Complessi (ICT.P11.004.001) (Modulo)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Information processing & management (Rivista)

Insieme di parole chiave di

• Keywords of "Soft-constrained inference for Named Entity Recognition" (Insieme di parole chiave)