Bill MacCartney


2014

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On the Importance of Text Analysis for Stock Price Prediction
Heeyoung Lee | Mihai Surdeanu | Bill MacCartney | Dan Jurafsky
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We investigate the importance of text analysis for stock price prediction. In particular, we introduce a system that forecasts companies’ stock price changes (UP, DOWN, STAY) in response to financial events reported in 8-K documents. Our results indicate that using text boosts prediction accuracy over 10% (relative) over a strong baseline that incorporates many financially-rooted features. This impact is most important in the short term (i.e., the next day after the financial event) but persists for up to five days.

2009

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An extended model of natural logic
Bill MacCartney | Christopher D. Manning
Proceedings of the Eight International Conference on Computational Semantics

2008

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Modeling Semantic Containment and Exclusion in Natural Language Inference
Bill MacCartney | Christopher D. Manning
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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A Phrase-Based Alignment Model for Natural Language Inference
Bill MacCartney | Michel Galley | Christopher D. Manning
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2007

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Learning Alignments and Leveraging Natural Logic
Nathanael Chambers | Daniel Cer | Trond Grenager | David Hall | Chloe Kiddon | Bill MacCartney | Marie-Catherine de Marneffe | Daniel Ramage | Eric Yeh | Christopher D. Manning
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing

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Natural Logic for Textual Inference
Bill MacCartney | Christopher D. Manning
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing

2006

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Generating Typed Dependency Parses from Phrase Structure Parses
Marie-Catherine de Marneffe | Bill MacCartney | Christopher D. Manning
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

This paper describes a system for extracting typed dependency parses of English sentences from phrase structure parses. In order to capture inherent relations occurring in corpus texts that can be critical in real-world applications, many NP relations are included in the set of grammatical relations used. We provide a comparison of our system with Minipar and the Link parser. The typed dependency extraction facility described here is integrated in the Stanford Parser, available for download.

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Learning to recognize features of valid textual entailments
Bill MacCartney | Trond Grenager | Marie-Catherine de Marneffe | Daniel Cer | Christopher D. Manning
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

2004

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Solving logic puzzles: From robust processing to precise semantics
Iddo Lev | Bill MacCartney | Christopher Manning | Roger Levy
Proceedings of the 2nd Workshop on Text Meaning and Interpretation