Generating narratives from financial data using active learning

使用主动学习从财务数据中生成叙述

基本信息

  • 批准号:
    531066-2018
  • 负责人:
  • 金额:
    $ 1.82万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Engage Grants Program
  • 财政年份:
    2018
  • 资助国家:
    加拿大
  • 起止时间:
    2018-01-01 至 2019-12-31
  • 项目状态:
    已结题

项目摘要

One of the major responsibilities of financial and business analysts is to generate narrative reports summarizing**business trends based on time series data. As it stands, this analysis is typically done manually, requiring**analysts to pour over huge amounts of data looking to understand trends, and performing ad hoc analyses to**establish relationships between data streams. As such, it is currently a high touch process and even with the**many analysts currently employed, important trends can go undetected for long periods of time. As in other**fields, a push toward automation has the potential to increase efficiency in the financial and business analysis**sector, increasing the speed of analysis, decreasing error rates, and freeing analysts from low-level tasks to**allow them to focus on high-level synthesis. One of the challenges in automating financial and business**analysis tasks is that analysts tend to rely on years of experience and domain specific knowledge to achieve**good results. Thus they rely on a "Gestalt" to lead their analysis, which is difficult to replicate using a pure**rule-based system. As such, Unilever Canada aims to develop a neural network based system for generating**narrative reports in natural language from numeric time series data, that will build on recent progress in text**generation from AI subfields such as machine translation, speech-to-text, image captioning and question**answering.
财务和业务分析师的主要职责之一是根据时间序列数据生成概述 ** 业务趋势的叙述性报告。就目前而言,这种分析通常是手动完成的,需要 ** 分析师倾倒大量数据以了解趋势,并执行特别分析以 ** 建立数据流之间的关系。因此,它目前是一个高度接触的过程,即使目前雇用了 ** 许多分析师,重要的趋势也可能在很长一段时间内未被发现。与其他领域一样,自动化有可能提高金融和商业分析领域的效率,提高分析速度,降低错误率,并将分析师从低级任务中解放出来,使他们能够专注于高级综合。自动化财务和业务分析任务的挑战之一是,分析师往往依赖多年的经验和特定领域的知识来实现良好的结果。因此,他们依赖于“完形”来引导他们的分析,这是很难用纯粹的基于规则的系统来复制的。因此,联合利华加拿大公司的目标是开发一种基于神经网络的系统,用于从数字时间序列数据中生成自然语言的叙述性报告,该系统将建立在人工智能子领域(如机器翻译,语音到文本,图像字幕和问题回答)的文本生成的最新进展基础上。

项目成果

期刊论文数量(0)
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会议论文数量(0)
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Bener, Ayse其他文献

Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting: An Application of Machine Learning Methods
  • DOI:
    10.1177/0272989x14535984
  • 发表时间:
    2015-08-01
  • 期刊:
  • 影响因子:
    3.6
  • 作者:
    Uyar, Asli;Bener, Ayse;Ciray, H. Nadir
  • 通讯作者:
    Ciray, H. Nadir

Bener, Ayse的其他文献

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{{ truncateString('Bener, Ayse', 18)}}的其他基金

Towards Measuring Defect Debt and Developing a Recommender System for Their Prioritization
衡量缺陷债务并开发优先级推荐系统
  • 批准号:
    RGPIN-2017-05312
  • 财政年份:
    2022
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Towards Measuring Defect Debt and Developing a Recommender System for Their Prioritization
衡量缺陷债务并开发优先级推荐系统
  • 批准号:
    RGPIN-2017-05312
  • 财政年份:
    2021
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Detecting similarities and conflicts in software requirements
检测软件需求中的相似性和冲突
  • 批准号:
    543936-2019
  • 财政年份:
    2021
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Collaborative Research and Development Grants
Towards Measuring Defect Debt and Developing a Recommender System for Their Prioritization
衡量缺陷债务并开发优先级推荐系统
  • 批准号:
    RGPIN-2017-05312
  • 财政年份:
    2020
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Detecting similarities and conflicts in software requirements
检测软件需求中的相似性和冲突
  • 批准号:
    543936-2019
  • 财政年份:
    2020
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Collaborative Research and Development Grants
Towards Measuring Defect Debt and Developing a Recommender System for Their Prioritization
衡量缺陷债务并开发优先级推荐系统
  • 批准号:
    RGPIN-2017-05312
  • 财政年份:
    2019
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Detecting similarities and conflicts in software requirements
检测软件需求中的相似性和冲突
  • 批准号:
    543936-2019
  • 财政年份:
    2019
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Collaborative Research and Development Grants
Test case prioritization
测试用例优先级
  • 批准号:
    499518-2016
  • 财政年份:
    2018
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Collaborative Research and Development Grants
Recommender system empowered by contextual information
由上下文信息支持的推荐系统
  • 批准号:
    490782-2015
  • 财政年份:
    2018
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Collaborative Research and Development Grants
Towards Measuring Defect Debt and Developing a Recommender System for Their Prioritization
衡量缺陷债务并开发优先级推荐系统
  • 批准号:
    RGPIN-2017-05312
  • 财政年份:
    2018
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual

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