These include a simple semantic space, where a word's meaning is a vector of co-occurrences with neighboring words (Mitchell and Lapata, 2010), a syntax-aware space based on weighted distributional triples that encode typed co-occurrence relations among words (Baroni and Lenci, 2010) and word embeddings computed with a neural language model (Bengio, 2001;Collobert and Weston, 2008) For all three models we used parameters that have been reported in the literature as optimal.
for the simple co-occurrence-based space we follow the settings of Mitchell and Lapata (2010): a context window of five words on either side of the target word and 2,000 vector dimensions (i.e., the 2000 most common context words in the BNC).