| Abstract | We propose a minimally supervised bootstrapping algorithm that uses a single seed and a recursive lexico-syntactic pattern to learn the arguments and the supertypes of a diverse set of semantic relations from the Web. |
| Introduction | Given these considerations, we address in this paper the following question: How can the selec-ti0nal restrictions of semantic relations be learned automatically from the Web with minimal eflort using lexico-syntactic recursive patterns? |
| Introduction | 0 A novel representation of semantic relations using recursive lexico-syntactic patterns. |
| Introduction | Section 3 addresses the representation of semantic relations using recursive patterns. |
| Recursive Patterns | Learned terms can then be replaced into the seed position automatically, creating a recursive procedure that is reportedly much more accurate and has much higher final yield. |
| Recursive Patterns | No other study has described the use or effect of recursive patterns for different semantic relations. |
| Recursive Patterns | Therefore, going beyond (Kozareva et al., 2008; Hovy et al., 2009), we here introduce recursive patterns other than DAP that use only one seed to harvest the arguments and supertypes of a wide variety of relations. |
| Conditional Random Fields | and backward recursions |
| Conditional Random Fields | These recursions require a number of operations that grows quadratically with |
| Conditional Random Fields | One advantage of the resulting algorithm, termed BCD in the following, is that the update of 6],, only involves carrying out the forward-backward recursions for the set of sequences that contain symbols cc such that at least one {fk(yl,y,$)}(y,y/)EY2 is non null, which can be much smaller than the whole training set. |
| Introduction | (2009), who use approximations to simplify the forward-backward recursions . |
| The Structural Semantic Relatedness Measure | This definition is recursive , and the starting point we choose is the semantic relatedness in the edge. |
| The Structural Semantic Relatedness Measure | Thus our structural semantic relatedness has two components: the neighbor term of the previous recursive phase which captures the graph structure and the semantic relatedness which captures the edge information. |
| The Structural Semantic Relatedness Measure | Thus, the recursive form of the structural semantic relatedness 5,; between the node i and the node j can be written as: |