A Hybrid Approach to Relationship Extraction from Stories. (2016) - Devisree, V.
Abstract: A story may be analyzed to identify the main characters and to extract the relationship between them. Relation extraction problems are generally solved either through supervised or unsupervised learning algorithms. In the former, there should be a text corpus for which the entities and their relation types are already known. Such algorithms typically learn to classify new entity pairs into any of the relation types it has already seen, based on some recurring patterns. On the other hand, the unsupervised learning approach is used when there is no such marked up corpus. Such algorithms typically identify patterns relevant to the relation extraction task, occurring within the corpus and then use these patterns to group entities such that the entities within a group share similar relationships. The proposed method is a hybrid approach which combines the features of unsupervised and supervised learning methods. It also uses some rules to extract relationships. The method identifies the main characters and collects the sentences related to them. Then these sentences are analyzed and classified to extract relationships. The main applications are story summarization and analysis of the major characters in stories.