Internal and external evidence in the identification and semantic categorization of proper names
Corpus processing for lexical acquisition
Word segmentation and recognition for web document framework
Proceedings of the eighth international conference on Information and knowledge management
Transition network grammars for natural language analysis
Communications of the ACM
Named Entity recognition without gazetteers
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Using predicate-argument structures for information extraction
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Locating complex named entities in web text
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
Natural language technology for information integration in business intelligence
BIS'07 Proceedings of the 10th international conference on Business information systems
Ontology-based information extraction for business intelligence
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
A method for automating the extraction of specialized information from the web
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
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This paper presents a novel linguistic information extraction approach exploiting analysts' stock ratings for statistical decision making. Over a period of one year, we gathered German stock analyst reports in order to determine market trends. Our goal is to provide business statistics over time to illustrate market trends for a user-selected company. We therefore recognize named entities within the very short stock analyst reports such as organization names (e.g. BASF, BMW, Ericsson), analyst houses (e.g. Gartner, Citigroup, Goldman Sachs), ratings (e.g. buy, sell, hold, underperform, recommended list) and price estimations by using lexicalized finite-state graphs, so-called local grammars. Then, company names and their acronyms respectively have to be cross-checked against data the analysts provide. Finally, all extracted values are compared and presented into charts with different views depending on the evaluation criteria (e.g. by time line). Thanks to this approach it will be easier and even more comfortable in the future to pay attention to analysts' buy/sell signals without reading all their reports.