Does It Happen? Multi-hop Path Structures for Event Factuality Prediction with Graph Transformer Networks
The goal of Event Factuality Prediction (EFP) is to determine the factual degree of an event mention, representing how likely the event mention has happened in text. Current deep learning models has demonstrated the importance of syntactic and semantic structures of the sentences to identify important context words for EFP. However, the major problem with these EFP models is that they only encode the one-hop paths between the words (i.e., the direct connections) to form the sentence structures. In this work, we show that the multi-hop paths between the words are also necessary to compute the sentence structures for EFP. To this end, we introduce a novel deep learning model for EFP that explicitly considers multi-hop paths with both syntax-based and semantic-based edges between the words to obtain sentence structures for representation learning in EFP. We demonstrate the effectiveness of the proposed model via the extensive experiments in this work.
Code (0)
등록된 구현이 없습니다.
Tasks
Representation LearningSentenceSimilar Papers 제목 키워드 기반
HapPenIng: Happen, Predict, Infer -- Event Series Completion in a Knowledge Graph
Event series, such as the Wimbledon Championships and the US presidential elections, represent important happenings in key societal areas including sports, culture and politics. However, semantic reference sources, such …
Cultural Vocal Bursts Intensity PredictionKnowledge GraphsFine-grained Event Categorization with Heterogeneous Graph Convolutional Networks
Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with co…
ClusteringEvent DetectionConnecting the Dots: Event Graph Schema Induction with Path Language Modeling
Event schemas can guide our understanding and ability to make predictions with respect to what might happen next. We propose a new Event Graph Schema, where two event types are connected through multiple paths involving …
Language ModelingLanguage ModellingThe Algorithmic Imprint
When algorithmic harms emerge, a reasonable response is to stop using the algorithm to resolve concerns related to fairness, accountability, transparency, and ethics (FATE). However, just because an algorithm is removed …
EthicsFairnessNegPSpan: efficient extraction of negative sequential patterns with embedding constraints
Mining frequent sequential patterns consists in extracting recurrent behaviors, modeled as patterns, in a big sequence dataset. Such patterns inform about which events are frequently observed in sequences, i.e. what does…