Generating finite-state transducers for semi-structured data extraction from the Web
Information Systems - Special issue on semistructured data
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Text classification using string kernels
The Journal of Machine Learning Research
Kernel methods for relation extraction
The Journal of Machine Learning Research
Bottom-up relational learning of pattern matching rules for information extraction
The Journal of Machine Learning Research
Named Entity recognition without gazetteers
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
GATE: an architecture for development of robust HLT applications
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Learning structured prediction models: a large margin approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Foundations and Trends in Databases
Open information extraction for the web
Open information extraction for the web
Analysis of a probabilistic model of redundancy in unsupervised information extraction
Artificial Intelligence
Collective entity linking in web text: a graph-based method
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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Everyday people are exchanging a huge amount of data through the Internet. Mostly, such data consist of unstructured texts, which often contain references to structured information (e.g., person names, contact records, etc.). In this work, we propose a novel solution to discover social events from actual press news edited by humans. Concretely, our method is divided in two steps, each one addressing a specific Information Extraction (IE) task: first, we use a technique to automatically recognize four classes of named-entities from press news: DATE, LOCATION, PLACE, and ARTIST. Furthermore, we detect social events by extracting ternary relations between such entities, also exploiting evidence from external sources (i.e., the Web). Finally, we evaluate both stages of our proposed solution on a real-world dataset. Experimental results highlight the quality of our first-step Named-Entity Recognition (NER) approach, which indeed performs consistently with state-of-the-art solutions. Eventually, we show how to precisely select true events from the list of all candidate events (i.e., all the ternary relations), which result from our second-step Relation Extraction (RE) method. Indeed, we discover that true social events can be detected if enough evidence of those is found in the result list of Web search engines.