Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010) |
| Editors | Chu-Ren Huang, Dan Jurafsky |
| Pages | 1128-1136 |
| Number of pages | 9 |
| Publication status | Published - 2010 |
| Externally published | Yes |
| Event | 23rd International Conference on Computational Linguistics, Coling 2010 - Beijing, China Duration: 23 Aug 2010 → 27 Aug 2010 |
Abstract
Strategic business decision making involves the analysis of market forecasts. Today, the identification and aggregation of relevant market statements is done by human experts, often by analyzing documents from the World Wide Web. We present an efficient information extraction chain to automate this complex natural language processing task and show results for the identification part. Based on time and money extraction, we identify sentences that represent statements on revenue using support vector classification. We provide a corpus with German online news articles, in which more than 2,000 such sentences are annotated by domain experts from the industry. On the test data, our statement identification algorithm achieves an overall precision and recall of 0.86 and 0.87 respectively.
ASJC Scopus subject areas
- Arts and Humanities(all)
- Language and Linguistics
- Computer Science(all)
- Computational Theory and Mathematics
- Social Sciences(all)
- Linguistics and Language
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Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010). ed. / Chu-Ren Huang; Dan Jurafsky. 2010. p. 1128-1136.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research
}
TY - GEN
T1 - Efficient Statement Identification for Automatic Market Forecasting
AU - Wachsmuth, Henning
AU - Prettenhofer, Peter
AU - Stein, Benno
PY - 2010
Y1 - 2010
N2 - Strategic business decision making involves the analysis of market forecasts. Today, the identification and aggregation of relevant market statements is done by human experts, often by analyzing documents from the World Wide Web. We present an efficient information extraction chain to automate this complex natural language processing task and show results for the identification part. Based on time and money extraction, we identify sentences that represent statements on revenue using support vector classification. We provide a corpus with German online news articles, in which more than 2,000 such sentences are annotated by domain experts from the industry. On the test data, our statement identification algorithm achieves an overall precision and recall of 0.86 and 0.87 respectively.
AB - Strategic business decision making involves the analysis of market forecasts. Today, the identification and aggregation of relevant market statements is done by human experts, often by analyzing documents from the World Wide Web. We present an efficient information extraction chain to automate this complex natural language processing task and show results for the identification part. Based on time and money extraction, we identify sentences that represent statements on revenue using support vector classification. We provide a corpus with German online news articles, in which more than 2,000 such sentences are annotated by domain experts from the industry. On the test data, our statement identification algorithm achieves an overall precision and recall of 0.86 and 0.87 respectively.
UR - http://www.scopus.com/inward/record.url?scp=80053428578&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:80053428578
SP - 1128
EP - 1136
BT - Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)
A2 - Huang, Chu-Ren
A2 - Jurafsky, Dan
T2 - 23rd International Conference on Computational Linguistics, Coling 2010
Y2 - 23 August 2010 through 27 August 2010
ER -