Abstract
Numerous studies have been conducted to extract relationships from different documents. However, extracting relationships from microblog posts is rarely studied. In this paper, we improve a novel kernel-based learning algorithm to mine the personae social relationships from microblog posts by combining the syntax and semantic meanings of the dependency trigram kernels (DTK). To deeply extract the personal social relationships of microblog posts, we define the relation feature words, provide seven rules for extracting these feature words, and propose a rule-based approach that mines these relation feature words from microblog posts. We construct relation feature word dictionaries for different relation types because of the lack of prominent relation features in microblog posts. We propose an algorithm to classify relation feature words by considering two features of the relation feature words, namely, syntax and semantic similarities between relation feature words in microblog posts and by using relation feature word dictionaries. Experimental results show that the average recall, precision, and F-measure of our proposed approach outperforms the original DTK in sentence selection, personae social relation extraction, and personae social relation classification. Finally, the relation graphs of five topics clarify that our proposed approach is effective for extracting personae social relations from microblog posts.
Highlights
The web is an important platform for searching useful information
PERSONAE SOCIAL RELATION EXTRACTION we introduce our approach for mining personaeśocial relations from microblog posts
The average Ps of the personae social relation extraction of the NDTK approach improved by approximately 10% compared with the original dependency trigram kernels (DTK) approach
Summary
The web is an important platform for searching useful information. At present, an increasing number of people are using social media, such as Twitter, Facebook and microblogs, which generates a large quantity of microtext information (such as microposts and videos) every day. We improve a novel kernel-based learning algorithm (denoted as the dependency trigram kernel, NDTK) to mine personae social relations This algorithm does not rely on entity information to train microblog posts. We propose a rule-based approach to mine these relation FWs for deeply extracting the personae social relation in microblog posts. The similarities are some important parameters for constructing the classifiers of the relations These approaches usually expressed these features of documents, sentences, words, phrases, and semantic meanings using nonlinear methods, such as tree structures. B. DTK ALGORITHM To extract personae social relations from the ACE corpus and Korean news, Choi and Kim [31] proposed the DTK algorithm based on the SVM.
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