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Machine Learning and Knowledge Extraction, Volume 2, Issue 4

December 2020 - 16 articles

Cover Story: Quantifying the extent to which Environmental, Social and Governance (ESG)-related conversations are carried out by companies is essential to objectively assess the impact of ESG on business operations. This research study detects historical trends in ESG discussions by analyzing the transcripts of corporate earning calls. It exploits recent advances in neural language modeling to understand the linguistic structure in ESG discourse. We develop a classification system that categorizes the relevance of a text sentence to ESG by fine-tuning a language model on sustainability reports. The semantic knowledge encoded in the classification model is then leveraged by applying it to the sentences in the conference transcripts using a novel distant-supervision approach. A trend analysis of earnings calls based on this transfer learning framework indicates that ESG factors are integral to business strategy. View this paper
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Articles (16)

  • Article
  • Open Access
24 Citations
6,787 Views
21 Pages

Hybrid simulation (HS) is an advanced simulation method that couples experimental testing and analytical modeling to better understand structural systems and individual components’ behavior under extreme events such as earthquakes. Conducting H...

  • Article
  • Open Access
32 Citations
11,079 Views
16 Pages

Mapping ESG Trends by Distant Supervision of Neural Language Models

  • Natraj Raman,
  • Grace Bang and
  • Armineh Nourbakhsh

The integration of Environmental, Social and Governance (ESG) considerations into business decisions and investment strategies have accelerated over the past few years. It is important to quantify the extent to which ESG-related conversations are car...

  • Article
  • Open Access
2,647 Views
17 Pages

Various big data sets are recorded on the server side of computer system. The big data are well defined as a volume, variety, and velocity (3V) model. The 3V model has been proposed by Gartner, Inc. as a first press release. 3V model means the volume...

  • Article
  • Open Access
4,665 Views
22 Pages

Less-Known Tourist Attraction Discovery Based on Geo-Tagged Photographs

  • Jhih-Yu Lin,
  • Shu-Mei Wen,
  • Masaharu Hirota,
  • Tetsuya Araki and
  • Hiroshi Ishikawa

Most existing studies of tourist attraction recommendations have specifically emphasized analyses of popular sites. However, recommending such spots encourages crowds to flock there in large numbers, making tourists feel uncomfortable. Furthermore, s...

  • Article
  • Open Access
7 Citations
3,911 Views
17 Pages

In this paper, we study how to extract visual concepts to understand landscape scenicness. Using visual feature representations from a Convolutional Neural Network (CNN), we learn a number of Concept Activation Vectors (CAV) aligned with semantic con...

  • Article
  • Open Access
7 Citations
6,611 Views
18 Pages

A Novel Ramp Metering Approach Based on Machine Learning and Historical Data

  • Saeed Ghanbartehrani,
  • Anahita Sanandaji,
  • Zahra Mokhtari and
  • Kimia Tajik

23 September 2020

The random nature of traffic conditions on freeways can cause excessive congestion and irregularities in the traffic flow. Ramp metering is a proven effective method to maintain freeway efficiency under various traffic conditions. Creating a reliable...

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Mach. Learn. Knowl. Extr. - ISSN 2504-4990