In this paper , we present work on extracting social networks from unstructured text . We introduce novel features derived from semantic annotations based on FrameNet . We also introduce novel semantic tree kernels that help us improve the performance of the best reported system on social event detection and classification by a statistically significant margin . We show results for combining the models for the two aforementioned subtasks into the overall task of social network extraction . We show that a combination of features from all three levels of abstractions ( lexical , syntactic and semantic ) are required to achieve the best performing system . Social network extraction from text has recently been gaining a considerable amount of attention ( Agarwal and Rambow , 2010 ; Elson et al . , 2010 ; Agarwal et al . , 2013a ; Agarwal et al . , 2013b ; He et al . , 2013 ) . One of the reason for this attention , we believe , is that being able to extract social networks from unstructured text may provide a powerful new tool for historians , political scientists , scholars of literature , and journalists to analyze large collections of texts around entities and their interactions . The tool would allow researchers to quickly extract networks and assess their size , nature , and cohesiveness , a task that would otherwise be impossible with corpora numbering millions of documents . It would also make it possible to make falsifiable claims about these networks , bringing the experimental method to disciplines like history , where it is still relatively rare . In our previous work ( Agarwal et al . , 2010 ) , we proposed a definition of a network based on interactions : nodes are entities and links are social events . We defined two broad types of links : one-directional links ( one person thinking about or talking about another person ) and bi-directional links ( two people having a conversation , a meeting , etc . ) . For example , in the following sentence , we would add two links to the network : a one-directional link between Toujan Faisal and the committee , triggered by the word said ( because Toujan is talking about the committee ) and a bi-directional link between the same entities triggered by the word informed ( a mutual interaction ) . ( 1 ) [ Toujan Faisal ] , 54 , said [ she ] was informed of the refusal by an [ Interior Ministry committee ] overseeing election preparations . In this paper , we extract networks using the aforementioned definition of social networks . We introduce and add tree kernel representations and features derived from frame-semantic parses to our previously proposed system . Our results show that hand-crafted frame semantic features , which are linguistically motivated , add less value to the overall performance in comparison with the frame-semantic tree kernels . We believe this is due to the fact that hand-crafted features require frame parses to be highly accurate and complete . In contrast , tree kernels are able to find and leverage less strict patterns without requiring the semantic parse to be entirely accurate or complete . Apart from introducing semantic features and tree structures , we evaluate on the task of social network extraction , which is a combination of two sub-tasks : social event detection and social event classification . In our previous work ( Agarwal and Rambow , 2010 ) , we presented results for the two sub-tasks , but no evaluation was presented for the task of social network extraction . We experiment with two different designs of combining models for the two sub-tasks : 1 ) One-versus-All and 2 ) Hierarchical . We find that the hierarchical design outperforms the more commonly used Oneversus-All by a statistically significant margin . Following are the contributions of this paper : 1 . We design and propose novel frame semantic features and tree-based representations and show that tree kernels are well suited to work with noisy semantic parses . 2 . We show that in order to achieve the best performing system , we need to include features and tree structures from all levels of abstractions , lexical , syntactic , and semantic , and that the convolution kernel framework is well-suited for creating such a combination . 3 . We combine the previously proposed subtasks ( social event detection and classification ) into a single task , social network extraction , and show that combining the models using a hierarchical design is significantly better than the one-versus-all design . The rest of the paper is structured as follows : In Section 2 , we give a precise definition of the task and describe the data . In Section 3 , we give a brief overview of frame semantics and motivate the need to use frame semantics for the tasks addressed in this paper . In Section 4 , we present semantic features and tree kernel representations designed for the tasks . In Section 5 , we briefly review tree kernels and support vector machines ( SVM ) . In Section 6 we present experiments and discuss the results . In Section 7 we discuss related work . We conclude and give future directions of work in Section 8 . This work has only scratched the surface of possibilities for using frame semantic features and tree structures for the task of social event extraction . We have shown that tree kernels are well suited to work with possibly inaccurate semantic parses in contrast to hand-crafted features that require the semantic parses to be completely accurate . We have also extended our previous work by designing and evaluating a full system for social network extraction . A more natural data representation for semantic parses is a graph structure . We are actively exploring the design of semantic graph structures that may be brought to bear with the use of graph kernels ( Vishwanathan et al . , 2010 ) .

Challenge: Social network extraction is gaining attention because it would be a powerful tool to analyze large collections of texts around entities and their interactions.
Approach: They propose to use tree kernel representations and features derived from frame-semantic parses for the task of social event extraction.
Outcome: They show that the use of semantic tree kernels is suited to noisy parses and improves the best system for social event detection and classification.