In search of meaning: Lessons, resources and next steps for computational analysis of financial discourse
In search of meaning: Lessons, resources and next steps for computational analysis of financial discourse
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DOI：
10.1111/jbfa.12378
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发表时间：
2019-03-01
影响因子：
2.9
通讯作者：
Simaki, Vasiliki
中科院分区：
文献类型：
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作者：
El-Haj, Mahmoud;Rayson, Paul;Simaki, Vasiliki
我们批判性地评估主流会计和金融研究，应用计算语言学 (CL) 的方法来研究金融话语。我们还回顾了文献中的共同主题和创新，并评估了应用 CL 方法相对于手动内容分析的研究的增量贡献。我们的分析得出的主要结论是： (a) 会计和金融研究总体上在 CL 方法方面落后于曲线，特别是在词义消歧方面； (b) 实施问题意味着 CL 所提议的好处往往不如支持者所建议的那么明显； (c) 结构性问题限制了实际意义； (d) CL 方法和高质量手动分析是分析金融话语的补充方法。我们描述了四种 CL 工具，这些工具尚未在主流 AF 研究中获得关注，但我们相信它们为加强金融话语意义的研究提供了有希望的方法。这四种工具分别是实体识别（NER）、摘要、语义和语料库语言学。
We critically assess mainstream accounting and finance research applying methods from computational linguistics (CL) to study financial discourse. We also review common themes and innovations in the literature and assess the incremental contributions of studies applying CL methods over manual content analysis. Key conclusions emerging from our analysis are: (a) accounting and finance research is behind the curve in terms of CL methods generally and word sense disambiguation in particular; (b) implementation issues mean the proposed benefits of CL are often less pronounced than proponents suggest; (c) structural issues limit practical relevance; and (d) CL methods and high quality manual analysis represent complementary approaches to analyzing financial discourse. We describe four CL tools that have yet to gain traction in mainstream AF research but which we believe offer promising ways to enhance the study of meaning in financial discourse. The four tools are named entity recognition (NER), summarization, semantics and corpus linguistics.