Towards Syntactic Iberian Polarity Classification DV was funded by MECD (FPU13/01180). MG is funded by a Juan de la Cierva grant (FJCI-2014-22853). CGR has received funding from the ERC, under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150). This research was supported by MINECO (FFI2014-51978-C2).
Table 5: Impact of the operations (O) with mono (sl) and multilingual lexica (ml) and of the ml with (+O) and without operations (-O).
['[BOLD] Lg', '[BOLD] O(SL)', '[BOLD] O(ML)', '[BOLD] ML(-O)', '[BOLD] ML(+O)']
Concerning the rules (columns 2 and 3), the results show that using the same set of universal rules improves the performance of the classifier in all the languages and settings. Their impact varies between 3.5 percentage points (ca) and more than 15 (es) and, for each language, the rules provide a similar effect in monolingual and multilingual lexica (except for ca, with much higher values in the ML scenario). On average, using multilingual lexica had a positive impact of 1.3 (-O) and 2.8 points (+O). As mentioned, ca has a different behaviour: the gain from rules when using monolingual lexica is about 3.50 points (lower than other languages), and the benefit of the ML lexicon without syntactic rules is of 4.25 points. However, when combining both the universal rules and the ML lexicon its performance increases ≈15 points, turning out that the combination of these two factors is decisive.