2 |
On the Use of Linguistic Features for the Evaluation of Generative Dialogue Systems ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
TorontoCL at CMCL 2021 Shared Task: RoBERTa with Multi-Stage Fine-Tuning for Eye-Tracking Prediction ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Quantifying the Task-Specific Information in Text-Based Classifications ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
An {E}valuation of {D}isentangled {R}epresentation {L}earning for {T}exts ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
How is BERT surprised? Layerwise detection of linguistic anomalies ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Comparing Pre-trained and Feature-Based Models for Prediction of Alzheimer's Disease Based on Speech
|
|
|
|
In: Front Aging Neurosci (2021)
|
|
BASE
|
|
Show details
|
|
8 |
Identification of primary and collateral tracks in stuttered speech
|
|
|
|
In: LREC 2020 - 12th Conference on Language Resources and Evaluation ; https://hal.archives-ouvertes.fr/hal-02959454 ; LREC 2020 - 12th Conference on Language Resources and Evaluation, May 2020, Marseille, France (2020)
|
|
BASE
|
|
Show details
|
|
9 |
Semantic coordinates analysis reveals language changes in the AI field ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
An information theoretic view on selecting linguistic probes ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
Examining the rhetorical capacities of neural language models ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
A textual analysis of US corporate social responsibility reports
|
|
|
|
Abstract:
We employ computer-based textual analysis to examine disclosure patterns for a sample of US corporate social responsibility (CSR) reports from the period 2002-2016. Starting from 466 features commonly used in computational linguistics, our results show that the linguistics or disclosure patterns in CSR reports can be used to accurately predict the actual CSR performance type of CSR reporters. Specifically, we find that the two most commonly used disclosure characteristics, number of words and number of sentences, alone can be used to predict reporting firms' CSR performance type with 81% accuracy. The accuracy of prediction increases to 96% when the top 50 linguistics features most relevant to firms' CSR performance are included in the prediction model. In addition, we find that the linguistic features of CSR disclosure identified by our study are incrementally value relevant to investors even after controlling for the actual CSR performance score from the professional CSR rating agencies. This finding suggests that the linguistic features of CSR disclosure can be an important venue for capital market participants in evaluating firms' CSR performance type, especially when professional CSR performance ratings are not available.
|
|
Keyword:
Companies; Complexity; Earnings; Environmental Performance; Financial Performance; Firm Value; Information; Nonfinancial Disclosure; Readability; Relevance
|
|
URL: https://espace.library.uq.edu.au/view/UQ:d3da462
|
|
BASE
|
|
Hide details
|
|
15 |
Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Representation Learning for Discovering Phonemic Tone Contours ...
|
|
|
|
BASE
|
|
Show details
|
|
18 |
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech ...
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Detecting cognitive impairments by agreeing on interpretations of linguistic features ...
|
|
|
|
BASE
|
|
Show details
|
|
20 |
Deconfounding age effects with fair representation learning when assessing dementia ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|