Share This Author
A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories
A new framework for evaluating story understanding and script learning: the `Story Cloze Test’, which requires a system to choose the correct ending to a four-sentence story, and a new corpus of 50k five- Sentence commonsense stories, ROCStories, to enable this evaluation.
Deterministic Coreference Resolution Based on Entity-Centric, Precision-Ranked Rules
- Heeyoung Lee, Angel X. Chang, Yves Peirsman, Nathanael Chambers, M. Surdeanu, Dan Jurafsky
- Computer ScienceCL
- 1 December 2013
The two stages of the sieve-based architecture, a mention detection stage that heavily favors recall, followed by coreference sieves that are precision-oriented, offer a powerful way to achieve both high precision and high recall.
Unsupervised Learning of Narrative Event Chains
A three step process to learning narrative event chains using unsupervised distributional methods to learn narrative relations between events sharing coreferring arguments and introduces two evaluations: the narrative cloze to evaluate event relatedness, and an order coherence task to evaluate narrative order.
Stanford’s Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task
- Heeyoung Lee, Yves Peirsman, Angel X. Chang, Nathanael Chambers, M. Surdeanu, Dan Jurafsky
- Computer ScienceCoNLL Shared Task
- 23 June 2011
The coreference resolution system submitted by Stanford at the CoNLL-2011 shared task was ranked first in both tracks, with a score of 57.8 in the closed track and 58.3 in the open track.
Unsupervised Learning of Narrative Schemas and their Participants
An unsupervised system for learning narrative schemas, coherent sequences or sets of events whose arguments are filled with participant semantic roles defined over words to improve on previous results in narrative/frame learning and induce rich frame-specific semantic roles.
Dense Event Ordering with a Multi-Pass Architecture
New experiments on strongly connected event graphs that contain ∼10 times more relations per document than the TimeBank are presented and a shift away from the single learner to a sieve-based architecture that naturally blends multiple learners into a precision-ranked cascade of sieves is described.
An Annotation Framework for Dense Event Ordering
This paper proposes a new annotation process with a mechanism to force annotators to label connected graphs that generates 10 times more relations per document than the TimeBank, and its TimeBank-Dense corpus is larger than all current corpora.
A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories
A new framework for evaluating story understanding and script learning: the 'Story Cloze Test', which requires a system to choose the correct ending to a four-sentence story, and a new corpus of ~50k five- Sentence commonsense stories, ROCStories, to enable this evaluation.
Classifying Temporal Relations Between Events
A fully automatic two-stage machine learning architecture that learns temporal relations between pairs of events by learning the temporal attributes of single event descriptions, such as tense, grammatical aspect, and aspectual class.
CaTeRS: Causal and Temporal Relation Scheme for Semantic Annotation of Event Structures
- N. Mostafazadeh, Alyson Grealish, Nathanael Chambers, James F. Allen, Lucy Vanderwende
- Computer ScienceEVENTS@HLT-NAACL
- 1 June 2016
A novel semantic annotation framework, called Causal and Temporal Relation Scheme (CaTeRS), which is unique in simultaneously capturing a comprehensive set of temporal and causal relations between events.