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Hell Hath No Fury? Correcting Bias in the NRC Emotion Lexicon
There have been several attempts to create an accurate and thorough emotion lexicon in English, which identifies the emotional content of words.
A Comprehensive Evaluation and Correction of the TimeBank Corpus
TimeML is an annotation scheme for capturing temporal information in text.
Inducing Stereotypical Character Roles from Plot Structure
Stereotypical character roles-also known as archetypes or dramatis personae-play an important function in narratives: they facilitate efficient communication with bundles of default characteristics and associations and ease understanding of those characters’ roles in the overall narrative.
Holistic Evaluation of Automatic TimeML Annotators
TimeML is a scheme for representing temporal information (times, events, & temporal relations) in texts.
Distinguishing Between Foreground and Background Events in News
Determining whether an event in a news article is a foreground or background event would be useful in many natural language processing tasks, for example, temporal relation extraction, summarization, or storyline generation.
A Straightforward Approach to Narratologically Grounded Character Identification
One of the most fundamental elements of narrative is character: if we are to understand a narrative, we must be able to identify the characters of that narrative.
Systematic Evaluation of a Framework for Unsupervised Emotion Recognition for Narrative Text
Close inspection of that work, however, revealed significant reproducibility problems, and we were unable to reimplement Kim{'}s approach as described.
Improving the Identification of the Discourse Function of News Article Paragraphs
Identifying the discourse structure of documents is an important task in understanding written text.
New Insights into Cross-Document Event Coreference: Systematic Comparison and a Simplified Approach
Cross-Document Event Coreference (CDEC) is the task of finding coreference relationships between events in separate documents, most commonly assessed using the Event Coreference Bank+ corpus (ECB+).
Evaluating Information Loss in Temporal Dependency Trees
Temporal Dependency Trees (TDTs) have emerged as an alternative to full temporal graphs for representing the temporal structure of texts, with a key advantage being that TDTs can be straightforwardly computed using adapted dependency parsers.
Detecting Subevents using Discourse and Narrative Features
Recognizing the internal structure of events is a challenging language processing task of great importance for text understanding.
Character Identification Refined: A Proposal
The most important of the two corpora is a set of 46 Russian folktales, on which the model achieves an F1 of 0. 81.
Identifying the Discourse Function of News Article Paragraphs
We test two hypotheses: first, that people can reliably annotate news articles with van Dijk{'}s theory; second, that we can reliably predict these labels using machine learning.
A New Approach to Animacy Detection
The system achieves an F1 of 0. 88 for classifying the animacy of referring expressions, which is comparable to state of the art results for classifying the animacy of words, and achieves an F1 of 0. 75 for classifying the animacy of coreference chains themselves.
A Simpler and More Generalizable Story Detector using Verb and Character Features
Story detection is the task of determining whether or not a unit of text contains a story.
The N2 corpus: A semantically annotated collection of Islamist extremist stories
We describe the N2 (Narrative Networks) Corpus, a new language resource.
