Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation
This addresses a limitation in relation schema induction for natural language processing, moving beyond binary relations to more informative higher-order ones, though it is an incremental advancement in method.
The paper tackles the problem of Higher-order Relation Schema Induction (HRSI) to identify type signatures for all arguments of relations with more than two arguments from unlabeled text, proposing the Tensor Factorization with Back-off and Aggregation (TFBA) framework, which is shown to handle sparsity and induce higher-order schemata in experiments on three real-world datasets.
Relation Schema Induction (RSI) is the problem of identifying type signatures of arguments of relations from unlabeled text. Most of the previous work in this area have focused only on binary RSI, i.e., inducing only the subject and object type signatures per relation. However, in practice, many relations are high-order, i.e., they have more than two arguments and inducing type signatures of all arguments is necessary. For example, in the sports domain, inducing a schema win(WinningPlayer, OpponentPlayer, Tournament, Location) is more informative than inducing just win(WinningPlayer, OpponentPlayer). We refer to this problem as Higher-order Relation Schema Induction (HRSI). In this paper, we propose Tensor Factorization with Back-off and Aggregation (TFBA), a novel framework for the HRSI problem. To the best of our knowledge, this is the first attempt at inducing higher-order relation schemata from unlabeled text. Using the experimental analysis on three real world datasets, we show how TFBA helps in dealing with sparsity and induce higher order schemata.