Automatic segmentation of text into structured records
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
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
Mining reference tables for automatic text segmentation
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating Unstructured Data into Relational Databases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Answering Imprecise Queries over Autonomous Web Databases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Information extraction from research papers using conditional random fields
Information Processing and Management: an International Journal
Information Processing and Management: an International Journal
FLUX-CIM: flexible unsupervised extraction of citation metadata
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Context-aware wrapping: synchronized data extraction
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Foundations and Trends in Databases
A flexible approach for extracting metadata from bibliographic citations
Journal of the American Society for Information Science and Technology
ONDUX: on-demand unsupervised learning for information extraction
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
A trigram hidden Markov model for metadata extraction from heterogeneous references
Information Sciences: an International Journal
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Information extraction by text segmentation (IETS) applies to cases in which data values of interest are organized in implicit semi-structured records available in textual sources (e.g. postal addresses, bibliographic information, ads). It is an important practical problem that has been frequently addressed in the recent literature. We report here partial results from a PhD thesis work in which we introduce ONDUX (On Demand Unsupervised Information Extraction), a new unsupervised probabilistic approach for IETS. As other unsupervised IETS approaches, ONDUX relies on information available on pre-existing data to associate segments in the input string with attributes of a given domain. Unlike other approaches, we rely on very effective matching strategies instead of explicit learning strategies. The effectiveness of this matching strategy is also exploited to disambiguate the extraction of certain attributes through a reinforcement step that explores sequencing and positioning of attribute values directly learned on-demand from test data, with no previous human-driven training, a feature unique to ONDUX. This assigns to ONDUX a high degree of flexibility and results in superior effectiveness, as demonstrated by experimental evaluation we have carried out with textual sources from different domains, in which ONDUX is compared with a state-of-art IETS approach.