C4.5: programs for machine learning
C4.5: programs for machine learning
WordNet: a lexical database for English
Communications of the ACM
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
ACM SIGIR Forum
Acquisition of Linguistic Patterns for Knowledge-Based Information Extraction
IEEE Transactions on Knowledge and Data Engineering
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
Algorithms on Strings
Identifying generic routings for product families based on text mining and tree matching
Decision Support Systems
Textual analysis of stock market prediction using breaking financial news: The AZFin text system
ACM Transactions on Information Systems (TOIS)
Decision support for determining veracity via linguistic-based cues
Decision Support Systems
Shallow semantic labeling using two-phase feature-enhanced string matching
Expert Systems with Applications: An International Journal
Hierarchical hidden Markov models for information extraction
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A scalable decision tree system and its application in pattern recognition and intrusion detection
Decision Support Systems
Sequence Data Mining
Automatically constructing a dictionary for information extraction tasks
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Tree topological features for unlexicalized parsing
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A machine learning parser using an unlexicalized distituent model
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
A semantically guided and domain-independent evolutionary model for knowledge discovery from texts
IEEE Transactions on Evolutionary Computation
Experiments with a differential semantics annotation for WordNet 3.0
Decision Support Systems
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Although most quantitative financial data are analyzed using traditional statistical, artificial intelligence or data mining techniques, the abundance of online electronic financial news articles has opened up new possibilities for intelligent systems that can extract and organize relevant knowledge automatically in a usable format. Most information extraction systems require a hand-built dictionary of templates and thus need continual modification to accommodate new patterns that are observed in the text. In this research, we propose a novel text-based decision support system (DSS) that (i) extracts event sequences from shallow text patterns, and (ii) predicts the likelihood of the occurrence of events using a classifier-based inference engine. The prediction relies on two major, but complementary, feature sets: adjacent events and a set of information-theoretic functions. In contrast to other approaches, the proposed text-based DSS gives explanatory hypotheses about its predictions from a coalition of intimations learned from the inference engine, while preserving robustness and without indulging in formalism. We investigate more than 2000 financial reports with 28,000 sentences. Experiments show that the prediction accuracy of our model outperforms similar statistical models by 7% for the seen data while significantly improving the prediction accuracy for the unseen data. Further comparisons substantiate the experimental findings.