Learning text analysis rules for domain-specific natural language processing
Learning text analysis rules for domain-specific natural language processing
Ontology-based extraction and structuring of information from data-rich unstructured documents
Proceedings of the seventh international conference on Information and knowledge management
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Relational learning of pattern-match rules for information extraction
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The use of word sense disambiguation in an information extraction system
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Hierarchical Wrapper Induction for Semistructured Information Sources
Autonomous Agents and Multi-Agent Systems
S-CREAM - Semi-automatic CREAtion of Metadata
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Active Learning for Natural Language Parsing and Information Extraction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Information Extraction with HMM Structures Learned by Stochastic Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Active Hidden Markov Models for Information Extraction
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
A maximum entropy approach to information extraction from semi-structured and free text
Eighteenth national conference on Artificial intelligence
Relational learning techniques for natural language information extraction
Relational learning techniques for natural language information extraction
Machine learning for information extraction in informal domains
Machine learning for information extraction in informal domains
Bottom-up relational learning of pattern matching rules for information extraction
The Journal of Machine Learning Research
A novel use of statistical parsing to extract information from text
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Toward general-purpose learning for information extraction
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Automatic pattern acquisition for Japanese information extraction
HLT '01 Proceedings of the first international conference on Human language technology research
Probabilistic reasoning for entity & relation recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Active learning with strong and weak views: a case study on wrapper induction
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Bayesian information extraction network
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Hierarchical hidden Markov models for information extraction
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Relational learning via propositional algorithms: an information extraction case study
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
CRYSTAL inducing a conceptual dictionary
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A case-based approach to knowledge acquisition for domain-specific sentence analysis
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Incremental information extraction using tree-based context representations
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Uncertainty management in rule-based information extraction systems
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
A comparison of tagging strategies for statistical information extraction
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Service-oriented information extraction
Proceedings of the 2011 Joint EDBT/ICDT Ph.D. Workshop
Automatic rule learning exploiting morphological features for named entity recognition in Turkish
Journal of Information Science
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Most of the information stored in digital form is hidden in natural language texts. Extracting and storing it in a formal representation (e.g. in form of relations in databases) allows efficient querying, easy administration and further automatic processing of the extracted data. The area of information extraction (IE) comprises techniques, algorithms and methods performing two important tasks: finding (identifying) the desired, relevant data and storing it in appropriate form for future use. The rapidly increasing number and diversity of IE systems are the evidence of continuous activity and growing attention to this field. At the same time it is becoming more and more difficult to overview the scope of IE, to see advantages of certain approaches and differences to others. In this paper we identify and describe promising approaches to IE. Our focus is adaptive systems that can be customized for new domains through training or the use of external knowledge sources. Based on the observed origins and requirements of the examined IE techniques a classification of different types of adaptive IE systems is established.