From Text to Lexicon: Bridging the Gap betweenWord Embeddings and Lexical Resources
Table 5: WCS performance, shared vocabulary, k=1. Best results across VSMs in bold.
['[EMPTY]', 'WN-N P', 'WN-N R', 'WN-N F', 'WN-V P', 'WN-V R', 'WN-V F', 'VN P', 'VN R', 'VN F']
Table 5 provides exact scores for reference. [CONTINUE] Note that the shared vocabulary setup puts the type and type.POS VSMs at advantage since it eliminates the effect of low coverage. Still, lemma-based targets significantly7 (p ≤ .005) outperform type-based targets in terms of F-measure in all cases. For window-based w2 contexts POS disambiguation yields significantly better F scores on lemmatized targets for VN (p ≤ .005) with borderline significance for WN-N and WN-V (p ≈ .05). When dependencybased dep contexts are used, the effect of POS disambiguation is only statistically significant on type targets for VN (p ≤ .005) and on lemma-based targets for WN-V (p ≤ .005). [CONTINUE] Lemma-based targets without POS disambiguation perform best on WN-N when dependency-based contexts are used; however, the difference to lemmatized and disambiguated targets is not statistically significant (p > .1).