2 research outputs found
How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis
Syntactic parsing, the process of obtaining the internal structure of
sentences in natural languages, is a crucial task for artificial intelligence
applications that need to extract meaning from natural language text or speech.
Sentiment analysis is one example of application for which parsing has recently
proven useful.
In recent years, there have been significant advances in the accuracy of
parsing algorithms. In this article, we perform an empirical, task-oriented
evaluation to determine how parsing accuracy influences the performance of a
state-of-the-art rule-based sentiment analysis system that determines the
polarity of sentences from their parse trees. In particular, we evaluate the
system using four well-known dependency parsers, including both current models
with state-of-the-art accuracy and more innacurate models which, however,
require less computational resources.
The experiments show that all of the parsers produce similarly good results
in the sentiment analysis task, without their accuracy having any relevant
influence on the results. Since parsing is currently a task with a relatively
high computational cost that varies strongly between algorithms, this suggests
that sentiment analysis researchers and users should prioritize speed over
accuracy when choosing a parser; and parsing researchers should investigate
models that improve speed further, even at some cost to accuracy.Comment: 19 pages. Accepted for publication in Artificial Intelligence Review.
This update only adds the DOI link to comply with journal's term
Formal models of Structure Building in Music, Language and Animal Songs
Human language, music and a variety of animal vocalisations constitute ways
of sonic communication that exhibit remarkable structural complexity. While the
complexities of language and possible parallels in animal communication have
been discussed intensively, reflections on the complexity of music and animal
song, and their comparisons are underrepresented. In some ways, music and
animal songs are more comparable to each other than to language, as
propositional semantics cannot be used as as indicator of communicative success
or well-formedness, and notions of grammaticality are less easily defined. This
review brings together accounts of the principles of structure building in
language, music and animal song, relating them to the corresponding models in
formal language theory, with a special focus on evaluating the benefits of
using the Chomsky hierarchy (CH). We further discuss common misunderstandings
and shortcomings concerning the CH, as well as extensions or augmentations of
it that address some of these issues, and suggest ways to move beyond.Comment: Pre-edited version of Zuidema, W., Hupkes, D., Wiggins, G. A.,
Scharff, C., & Rohrmeirer, M. (2018). Formal Models of Structure Building in
Music, Language, and Animal Song. The Origins of Musicality, 25