Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion
Table 1. A frequency submatrix ϕ of president entities and their co-occured skip-grams.
['Entities', 'President __ and', 'President __ ,', 'President __ said', 'President __ ’s', 'President __ *']
The infrequent “Gorbachev” does not have enough co-occurrence with type-indicative skip-grams and might be even filtered out from the candidate pool What’s more, the skip-gram of “President __ said” might be neglected in the context feature selection process due to its lack of interaction with president entities. These four different skip-grams are actually similar since they can all be filled in with a president entity. Therefore, if we can extract the meaningful part from the original window size, such as “President __” in this example, we will be able to merge these different skip-grams into a common and more flexible pattern: “President __ ∗”, which we named as a flexgram. The last term is an asteroid which serves as a wildcard term to be matched with any words. Then we can update the co-occurrence matrix by Φe,flex(c)=∑flex(c′)=flex(c)Φe,flex(c′) where flex(c) means the flexible transformation of local context c. Then we can merge those infrequent but type-indicative skip-grams together. However, one might say a trivial solution to this data sparsity issue is to exhaust a variety of window sizes, (e.g., [-2,+2],[-1,+1],[-1,0],[0,+1]), as has been done by previous studies(Shen2017SetExpanCS; Yu2019CaSE; Rong2016EgoSetEW). However, this approach very likely ends up generating many skip-grams that are too general. For example, the other half of “President __ and” is “__ and”, which only contains stop words, not informative enough in implying the semantic class of the “skipped” term.