Do We Need Word Sense Disambiguation for LCM Tagging

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.