Automatic Detection of Uncertain Statements in the Financial Domain
| Autor: | Simone Paolo Ponzetto, Christoph Kilian Theil, Heiner Stuckenschmidt, Sanja Štajner |
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| Rok vydání: | 2018 |
| Předmět: |
Finance
050208 finance Computer science business.industry media_common.quotation_subject 05 social sciences Binary number 050201 accounting Deception computer.software_genre Domain (software engineering) Task (project management) Information extraction 0502 economics and business business computer Sentence Spoken language media_common |
| Zdroj: | Computational Linguistics and Intelligent Text Processing ISBN: 9783319771151 CICLing (2) |
| DOI: | 10.1007/978-3-319-77116-8_48 |
| Popis: | The automatic detection of uncertain statements can benefit NLP tasks such as deception detection and information extraction. Furthermore, it can enable new analyses in social sciences such as business where the quantification of uncertainty or risk plays a significant role. Thus, for the first time, we approached the automatic detection of uncertain statements as a binary sentence classification task on the transcripts of spoken language in the financial domain. We created a new dataset and – besides using bag-of-words, part-of-speech tags, and dictionaries – developed rule-based features tailored to our task. Finally, we analyzed systematically, which features perform best in the financial domain as opposed to the previously researched encyclopedic domain. |
| Databáze: | OpenAIRE |
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