70,613 research outputs found
Statistical Inferences for Polarity Identification in Natural Language
Information forms the basis for all human behavior, including the ubiquitous
decision-making that people constantly perform in their every day lives. It is
thus the mission of researchers to understand how humans process information to
reach decisions. In order to facilitate this task, this work proposes a novel
method of studying the reception of granular expressions in natural language.
The approach utilizes LASSO regularization as a statistical tool to extract
decisive words from textual content and draw statistical inferences based on
the correspondence between the occurrences of words and an exogenous response
variable. Accordingly, the method immediately suggests significant implications
for social sciences and Information Systems research: everyone can now identify
text segments and word choices that are statistically relevant to authors or
readers and, based on this knowledge, test hypotheses from behavioral research.
We demonstrate the contribution of our method by examining how authors
communicate subjective information through narrative materials. This allows us
to answer the question of which words to choose when communicating negative
information. On the other hand, we show that investors trade not only upon
facts in financial disclosures but are distracted by filler words and
non-informative language. Practitioners - for example those in the fields of
investor communications or marketing - can exploit our insights to enhance
their writings based on the true perception of word choice
On Cognitive Preferences and the Plausibility of Rule-based Models
It is conventional wisdom in machine learning and data mining that logical
models such as rule sets are more interpretable than other models, and that
among such rule-based models, simpler models are more interpretable than more
complex ones. In this position paper, we question this latter assumption by
focusing on one particular aspect of interpretability, namely the plausibility
of models. Roughly speaking, we equate the plausibility of a model with the
likeliness that a user accepts it as an explanation for a prediction. In
particular, we argue that, all other things being equal, longer explanations
may be more convincing than shorter ones, and that the predominant bias for
shorter models, which is typically necessary for learning powerful
discriminative models, may not be suitable when it comes to user acceptance of
the learned models. To that end, we first recapitulate evidence for and against
this postulate, and then report the results of an evaluation in a
crowd-sourcing study based on about 3.000 judgments. The results do not reveal
a strong preference for simple rules, whereas we can observe a weak preference
for longer rules in some domains. We then relate these results to well-known
cognitive biases such as the conjunction fallacy, the representative heuristic,
or the recogition heuristic, and investigate their relation to rule length and
plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus
on plausibility and relation to interpretability, comprehensibility, and
justifiabilit
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