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    题名: Mining opinion holders and opinion patterns in US financial statements
    作者: Chen, Chien-Liang;Liu, Chao-Lin;Chang, Yuan-Chen;Tsai, Hsiang-Ping
    陳建良;劉昭麟;張元晨;蔡湘萍
    贡献者: 資科系;語言所
    关键词: Conditional random field;Information extraction;Opinion mining;Semantic labeling;Sentiment analysis;Text mining;Artificial intelligence;Finance;Image segmentation;Quality control;Random processes;Semantics;Data mining
    日期: 2011-11
    上传时间: 2015-04-08 17:34:09 (UTC+8)
    摘要: Subjective statements provide qualitative evaluation of the financial status of the reporting corporations, in addition to the quantitative information released in US 10-K filings. Both qualitative and quantitative appraisals are crucial for quality financial decisions. To extract such opinioned statements from the reports, we built tagging models based on the conditional random field (CRF) techniques, considering a variety of combinations of linguistic factors including morphology, orthography, predicate-argument structure, syntax and simple semantics. The CRF models showed reasonable effectiveness to find opinion holders in experiments when we adopted the popular MPQA corpus for training and testing. We also identified opinion patterns in the form of multi-word expressions (MWEs), which is a major contribution of our work. In a recent article published in a prestigious journal in Finance, single words, rather than MWEs, were reported to indicate positive and negative judgments in financial statements. © 2011 IEEE.
    關聯: Proceedings - 2011 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2011, 論文編號 6120721, 62-68 最佳論文佳作獎, 中華民國人工智慧學會
    10.1109/TAAI.2011.19
    数据类型: conference
    显示于类别:[資訊科學系] 會議論文

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