Word Embeddings for Entity-annotated Texts
Table 3: Categorization results. Shown is the purity of clusters obtained with k-means and agglomerative clustering (AC). The best two values are highlighted. For the raw text models, multi-word entity names are the mean of word vectors.
['k-means', 'ESSLLI_1a', '[ITALIC] rW2V  [BOLD] 0.575', '[ITALIC] rGLV 0.545', '[ITALIC] aW2V  [BOLD] 0.593', '[ITALIC] aGLV 0.454', 'DW [ITALIC] id 0.570', 'DW [ITALIC] log 0.520', 'VRS 0.534']
For the raw embeddings, we represent multi-word entities by the mean of the vectors of individual words in the entity’s name. In most tasks, rW2V and rGLV create clusters with the best purity, even for the entity-based datasets of cities, politicians, and companies. However, most purity values lie in the range from 0.45 to 0.65 and no method performs exceptionally poorly. Since only the words in the evaluation datasets are clustered, the results do not give us insight into the spatial mixing of terms and entities.