From Text to Lexicon: Bridging the Gap betweenWord Embeddings and Lexical Resources
Table 1: Benchmark performance, Spearman’s ρ. SGNS results with * taken from [morphfit]. Best results per column (benchmark) annotated for our setup only.
['Context: w2', 'Context: w2 SimLex', 'Context: w2 SimLex', 'Context: w2 SimLex', 'Context: w2 SimLex', 'Context: w2 SimVerb']
Table 1 summarizes the performance of the VSMs in question on similarity benchmarks. [CONTINUE] Lemmatized targets generally perform better, with the boost being more pronounced on SimVerb. [CONTINUE] Adding POS information benefits the SimVerb and SimLex verb performance, [CONTINUE] the type.POS targets show a considerable performance drop on SimVerb and SimLex verbs. [CONTINUE] Using dep-W contexts proves beneficial for both datasets [CONTINUE] We provide the Morph-Fitting scores (Vuli´c et al., 2017b) as a state-of-the-art reference; [CONTINUE] This approach uses word type-based VSMs specialized via Morph-Fitting (MFit), which can be seen as an alternative to lemmatization. Morph-Fitting consists of two stages: the Attract (A) stage brings word forms of the same word closer in the VSM, while the Repel (R) stage sets the derivational antonyms further apart. Lemma grouping is similar to the Attract stage. [CONTINUE] as the comparison of MFit-A and -AR shows, a major part of the Morph-Fitting performance gain on SimLex comes from the derivational Repel stage