How important is syntactic parsing accuracy? An empirical evaluation on rule-based sentiment analysis
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How important is syntactic parsing accuracy? An empirical evaluation on rule-based sentiment analysisFecha
2019Cita bibliográfica
Gómez-Rodríguez, C., Alonso-Alonso, I. & Vilares, D. How important is syntactic parsing accuracy? An empirical evaluation on rule-based sentiment analysis. Artif Intell Rev 52, 2081–2097 (2019). https://doi.org/10.1007/s10462-017-9584-0
Referenciado por
10.1007/s10462-017-9584-0
Resumen
[Abstract]: 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.
Palabras clave
Syntactic parsing
Sentiment analysis
Natural language processing
Artificial intelligence
Sentiment analysis
Natural language processing
Artificial intelligence
Descripción
This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10462-017-9584-0
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ISSN
0269-2821
1573-7462
1573-7462