메뉴 건너뛰기




Volumn , Issue , 2008, Pages 21-30

Regular expression learning for information extraction

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; INFORMATION RETRIEVAL; LEARNING ALGORITHMS; PATTERN MATCHING;

EID: 78651310094     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/1613715.1613719     Document Type: Conference Paper
Times cited : (146)

References (30)
  • 1
    • 26744467222 scopus 로고
    • Incremental grammatical inference from positive and negative data using unbiased finite state automata
    • R. Alquezar and A. Sanfeliu. 1994. Incremental grammatical inference from positive and negative data using unbiased finite state automata. In SSPR.
    • (1994) SSPR
    • Alquezar, R.1    Sanfeliu, A.2
  • 3
    • 84893833429 scopus 로고    scopus 로고
    • Inference of concise DTDs from XML data
    • Geert Jan Bex et al. 2006. Inference of concise DTDs from XML data. In VLDB.
    • (2006) VLDB
    • Bex, G.J.1
  • 4
    • 83755214761 scopus 로고    scopus 로고
    • Pattern-based disambiguation for natural language processing
    • Eric Brill. 2000. Pattern-based disambiguation for natural language processing. In SIGDAT.
    • (2000) SIGDAT
    • Brill, E.1
  • 5
    • 52449131705 scopus 로고    scopus 로고
    • Information extraction from theWorldWideWeb
    • William W. Cohen and Andrew McCallum. 2003. Information Extraction from theWorldWideWeb. in KDD
    • (2003) KDD
    • Cohen, W.W.1    McCallum, A.2
  • 7
    • 33746062171 scopus 로고    scopus 로고
    • Learning to understand web site update requests
    • William W. Cohen et al. 2005. Learning to Understand Web Site Update Requests. In IJCAI.
    • (2005) IJCAI
    • Cohen, W.W.1
  • 8
    • 84880859303 scopus 로고    scopus 로고
    • Adaptive information extraction from text by rule induction and generalization
    • Fabio Ciravegna. 2001. Adaptive information extraction from text by rule induction and generalization. In IJCAI.
    • (2001) IJCAI
    • Ciravegna, F.1
  • 10
    • 0742267047 scopus 로고    scopus 로고
    • Learning regular languages using RFSAs
    • Francois Denis et al. 2004. Learning regular languages using RFSAs. Theor. Comput. Sci., 313(2):267-294.
    • (2004) Theor. Comput. Sci. , vol.313 , Issue.2 , pp. 267-294
    • Denis, F.1
  • 11
    • 0035399494 scopus 로고    scopus 로고
    • Learning regular languages from simple positive examples
    • Francois Denis. 2001. Learning regular languages from simple positive examples. Machine Learning, 44(1/2):37-66.
    • (2001) Machine Learning , vol.44 , Issue.1-2 , pp. 37-66
    • Denis, F.1
  • 12
    • 84863338502 scopus 로고    scopus 로고
    • DBLife: A community information management platform for the database research community
    • Pedro DeRose et al. 2007. DBLife: A Community Information Management Platform for the Database Research Community In CIDR
    • (2007) CIDR
    • Derose, P.1
  • 13
    • 80053369676 scopus 로고    scopus 로고
    • Incremental regular inference
    • Pierre Dupont. 1996. Incremental regular inference. In ICGI.
    • (1996) ICGI
    • Dupont, P.1
  • 14
    • 65449144263 scopus 로고    scopus 로고
    • Self-supervised relation extraction from the web
    • Ronen Feldman et all. 2006. Self-supervised Relation Extraction from the Web. In ISMIS.
    • (2006) ISMIS
    • Feldman, R.1
  • 15
    • 80053352547 scopus 로고    scopus 로고
    • Algorithms for learning regular expressions
    • Henning Fernau. 2005. Algorithms for learning regular expressions. In ALT.
    • (2005) ALT
    • Fernau, H.1
  • 16
    • 80053380477 scopus 로고    scopus 로고
    • Learning regular languages from positive evidence
    • Laura Firoiu et al. 1998. Learning regular languages from positive evidence. In CogSci.
    • (1998) CogSci
    • Firoiu, L.1
  • 17
    • 0031633368 scopus 로고    scopus 로고
    • Toward information extraction: Identifying protein names from biological papers
    • K. Fukuda et al. 1998. Toward information extraction: identifying protein names from biological papers. Pac Symp Biocomput., 1998:707-718
    • (1998) Pac Symp Biocomput. , vol.1998 , pp. 707-718
    • Fukuda, K.1
  • 18
    • 80053360419 scopus 로고    scopus 로고
    • Learning regular expressions from noisy sequences
    • Ugo Galassi and Attilio Giordana. 2005. Learning regular expressions from noisy sequences. In SARA.
    • (2005) SARA
    • Galassi, U.1    Giordana, A.2
  • 19
    • 0000216094 scopus 로고    scopus 로고
    • XTRACT: A system for extracting document type descriptors from XML documents
    • Minos Garofalakis et al. 2000. XTRACT: a system for extracting document type descriptors from XML documents. In SIGMOD.
    • (2000) SIGMOD
    • Garofalakis, M.1
  • 20
    • 70350685696 scopus 로고    scopus 로고
    • Empirical study on the performance stability of named entity recognition model across domains
    • Hong Lei Guo et al. 2006. Empirical Study on the Performance Stability of Named Entity Recognition Model across Domains In EMNLP.
    • (2006) EMNLP
    • Guo, H.L.1
  • 21
    • 80053347889 scopus 로고    scopus 로고
    • Java Regular Expressions. 2008. http://java.sun.com/javase/6/docs/api/ java/util/regex/packagesummary html.
    • (2008)
  • 22
    • 85016671454 scopus 로고    scopus 로고
    • Named entity recognition with character-level models
    • Dan Klein et al. 2003. Named Entity Recognition with Character-Level Models. In HLT-NAACL.
    • (2003) HLT-NAACL
    • Klein, D.1
  • 23
    • 80053344402 scopus 로고    scopus 로고
    • An effective two-stage model for exploiting non-local dependencies in named entity recognition
    • Vijay Krishnan and Christopher D. Manning. 2006. An Effective Two-Stage Model for Exploiting Non-Local Dependencies in Named Entity Recognition. In ACL.
    • (2006) ACL
    • Krishnan, V.1    Manning, C.D.2
  • 24
    • 33750362018 scopus 로고    scopus 로고
    • Getting work done on the web: Supporting transactional queries
    • Yunyao Li et al. 2006. Getting work done on the web: Supporting transactional queries. In SIGIR.
    • (2006) SIGIR
    • Li, Y.1
  • 25
    • 0000747663 scopus 로고    scopus 로고
    • Maximum entropy markov models for information extraction and segmentation
    • Andrew McCallum et al. 2000. Maximum Entropy Markov Models for Information Extraction and Segmentation. In ICML.
    • (2000) ICML
    • McCallum, A.1
  • 26
    • 80053234295 scopus 로고    scopus 로고
    • Extracting personal names from emails: Applying named entity recognition to informal text
    • Einat Minkov et al. 2005. Extracting personal names from emails: Applying named entity recognition to informal text. In HLT/EMNLP.
    • (2005) HLT/EMNLP
    • Minkov, E.1
  • 27
    • 0032624184 scopus 로고    scopus 로고
    • Learning information extraction rules for semi-structured and free text
    • Stephen Soderland. 1999. Learning information extraction rules for semi-structured and free text. Machine Learning, 34:233-272.
    • (1999) Machine Learning , vol.34 , pp. 233-272
    • Soderland, S.1
  • 28
    • 0036678776 scopus 로고    scopus 로고
    • Tagging gene and protein names in biomedical text
    • Lorraine Tanabe and W. John Wilbur 2002. Tagging gene and protein names in biomedical text. Bioinformatics, 18:1124-1132.
    • (2002) Bioinformatics , vol.18 , pp. 1124-1132
    • Tanabe, L.1    Wilbur, W.J.2
  • 29
    • 13844299022 scopus 로고    scopus 로고
    • A semisupervised active learning algorithm for information extraction from textual data
    • Tianhao Wu and William M. Pottenger. 2005. A semisupervised active learning algorithm for information extraction from textual data. JASIST, 56(3):258-271.
    • (2005) JASIST , vol.56 , Issue.3 , pp. 258-271
    • Wu, T.1    Pottenger, W.M.2
  • 30


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.