Detecting experiences from weblogs
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A high-precision approach to detecting hedges and their scopes
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Event annotation schemes and event recognition in spanish texts
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
Modality and negation: An introduction to the special issue
Computational Linguistics
Are you sure that this happened? assessing the factuality degree of events in text
Computational Linguistics
Did it happen? the pragmatic complexity of veridicality assessment
Computational Linguistics
Cross-genre and cross-domain detection of semantic uncertainty
Computational Linguistics
Semantic frames as an anchor representation for sentiment analysis
WASSA '12 Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis
| Hi-index | 0.00 |
Event factuality is the level of information expressing the factual status of eventualities mentioned in text. That is, it conveys whether eventualities are characterized as corresponding to facts, to possibilities, or to situations that do not hold in the world. As such, it touches on two categories more standardly assumed in the literature: modality and evidentiality. They both have been widely discussed in linguistics and philosophy, but it is not until recently that have started to receive some attention within the area of NLP. Factuality is a necessary component for reasoning about eventualities in discourse. Inferences derived from events that have not happened, or that are possible, are different from those derived from events judged as factual. It is also essential for any task involving temporal ordering. The creation of event timelines needs to be aware of the different status of eventualities presented as uncertain or counterfactual. My dissertation aims at designing and developing a factuality profiler, namely a tool devoted to the identification of the factuality degree associated to eventualities mentioned in discourse. Event factuality cannot be conceived independently from language users, who are understood here as the sources of factuality information. Their inclusion in the model is fundamental. Two sources can assign different factuality values to the same event. Because of that, the factuality profiler must be capable of representing different and possibly contradictory information about the factuality nature of any event. De Facto, the tool I am presenting here, is grounded on the linguistic strategies we speakers employ to signal degrees of factuality in discourse. These involve information at different levels: lexical, syntactic, and rhetoric. De Facto implements an algorithm based on the grammatical structuring of factuality in languages like English, and is informed with a set of linguistic resources compiled from a data-driven approach. For evaluating De Facto, I created FactBank, a corpus annotated with factuality information. The interannotation agreement score for the task of assigning factuality values to events is kcohen = 0.81. Running De Facto against the gold standard results in F1=0.74 (macro-averaging), F1=0.85 (micro-averaging) and, in terms of interannotation agreement, kcohen =0.72.