Abstract
Observing the current state of natural language processing, especially in the Polish language, one notices that sense-level dictionaries are becoming increasingly popular. For instance, the largest manually annotated sentiment dictionary for Polish is now based on plWordNet (the Polish WordNet) [13], also the Polish Linguistic Category Model (LCM-PL) [10] dictionary has its significant part annotated on sense level. Our paper addresses the important question: what is the influence of word sense disambiguation in real-world scenarios and how it compares to the simpler baseline of labeling using just the tag of the most frequent sense. We evaluate both approaches on data sets compiled for studies on fake opinion detection and predicting levels of self-esteem in the area of social psychology. Our conclusion is that the baseline method vastly outperforms its competitor.
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Notes
- 1.
Our data sets have been processed in March 2018. We have no information about version of the WSD module available at that time, including no information on potential open bugs that might influence sense annotation.
- 2.
The study was conducted by members of the Warsaw Evaluative Learning Lab headed by professor Robert Balas.
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Wawer, A., Sarzyńska, J. (2018). Do We Need Word Sense Disambiguation for LCM Tagging?. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2018. Lecture Notes in Computer Science(), vol 11107. Springer, Cham. https://doi.org/10.1007/978-3-030-00794-2_21
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