model for which the word “pear” has its largest weighting and whose most strongly positively associated tokens are “action”, “records”, “government”, “record”, and “search”. As can be seen, it is not clear at first sight what the relationships between the words in this dimension are or even to what semantic category they all belong in, making it hard to extract an interpretation from it.

To overcome this problem, some approaches for building more interpretable DSMs have been proposed. An example is Strudel (structured dimension extraction and labeling), a corpus-based semantic model that induces semantic information from naturally occurring data using part-of-speech (POS) tagging, lemmatization of the corpus, and a set of extraction templates defined over POS sequences. The model’s main goal is to extract dimensions which are “interpretable as properties, automatically annotated with information about the nature of the relation they instantiate” [@baroni2010strudel]. That means it involves some relation extraction functionality, but instead of being predefined, the relations are inferred from the co-occurrence patterns, that is, from the distribution of patterns connecting a concept to its properties. For example, for the concept “book”, Strudel associates the following properties, along with the correspondent relations (expressed by either verbs or prepositions): they 