A New Surprise Measure for Extracting Interesting Relationships between Persons (2021)

Abstract: One way to enhance user engagement in search engines is to suggest interesting facts to the user. Although relationships between persons are important as a target for text mining, there are few effective approaches for extracting the interesting relationships between persons. We therefore propose a method for extracting interesting relationships between persons from natural language texts by focusing on their surprisingness. Our method first extracts all personal relationships from dependency trees for the texts and then calculates surprise scores for distributed representations of the extracted relationships in an unsupervised manner. The unique point of our method is that it does not require any labeled dataset with annotation for the surprising personal relationships. The results of the human evaluation show that the proposed method could extract more interesting relationships between persons from Japanese Wikipedia articles than a popularity-based baseline method. We demonstrate our proposed method as a chrome plugin on google search.

## introduction interesting facts are useful information for a variety of important tasks. for example, in data mining, the interesting facts can enhance user engagement in search engines @xcite . in natural language processing, the interesting facts can improve user experience with automatic conversation systems @xcite . however, if we rely on experts to gather the interesting facts, the cost becomes quite high. as a solution, several approaches have been developed to extract interesting facts automatically. @xcite proposed a set of unsupervised link discovery methods that can compute interestingness on graph data represented as a set of entities connected by a set of binary relations. ex.1: tim burton and johnny depp when tim burton met johnny depp for the first time, he had the impression that johnny depp was a hopelessly poor actor. ex.2: chien-ming wang and suzuki ichiro chien-ming wang, a major-leaguer from taiwan, asked suzuki ichiro three autographs before the start of the game. ## extracting interesting relationships between persons figure 3 provides an overview of the entire process of extracting sentences that may include interesting personal relationships about a target person from given documents. the extraction procedure is as follows: the details of each step are described in the following subsections. ## experiments we conducted human evaluation to determine how well our proposed method can extract interesting relationships between persons. the next subsections describe the details of our experimental settings and the evaluation results. ## demonstration system as shown in figure 2 , our demonstration system presents the top five interesting relationships between persons at the top of the search results based on the current search query. this demonstration system consists of server and client sides. the working process of the system follows the order: the client-side was implemented on jquery libraries, and the server-side was implemented on python 3.0 with utilizing http.server module. we chose pop+com+sup as our demonstration system because this model achieved the best result in the human evaluation in the cases of @xmath0, 2, and 3. ## related work there have been several approaches for extracting interesting facts. we can divide them into supervised and unsupervised approaches. the unsupervised approaches have been commonly used for this type of extraction. @xcite proposed a method that mines good trivia questions from a relational database based on predefined rules. @xcite proposed a set of unsupervised link discovery methods that can compute interestingness on graph data that is represented as a set of entities connected by a set of binary relations. @xcite proposed an algorithm that automatically mines trivia facts from wikipedia by utilizing its category structure. their approach can rank the entity's categories by their trivia quality, which is induced by the category. @xcite mined trivia facts from superlative tables in wikipedia articles. they utilized a template-based approach for semi-automatically generating natural language statements as fun facts. their work had actually been incorporated into the search engine by google. @xcite proposed a method to obtain sentences including trivia facts by focusing on a tendency of the wikipedia article structure that a paragraph containing trivial facts is not similar to other paragraphs in a article. the supervised approaches have also been used for extracting interesting facts. @xcite proposed models that predict the level of interest a user gives to various text spans in a document by observing the user's browsing behavior via clicks from one page to another. @xcite constructed a labeled dataset for movie entities and proposed a method for extracting interesting sentences from wikipedia articles and ordering them based on interestingness by utilizing rank-svm trained with the constructed dataset. @xcite proposed a method for automatically mining trivia facts for an entity of a given domain in knowledge graphs by utilizing deep convolutional neural networks trained in a supervised manner. ## conclusion in this paper, we proposed a method for extracting interesting relationships between persons from natural language texts in an unsupervised manner. human evaluation of the personal relationships extracted from japanese wikipedia articles showed that the proposed method improved the interestingness compared to a popularity-based baseline. through the result, we can conclude that considering the surprisingness of relationships between persons is effective in improving the interestingness of the extracted results. furthermore, to demonstrate our proposed method, we incorporated the method into a google chrome plugin, which can work on google search. as future work, we will investigate ways to extract personal relationships based on more detailed information about a dependency tree.