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. Relative to temporal graphs, the tree form of TDTs naturally omits some fraction of temporal relationships, which intuitively should decrease the amount of temporal information available, potentially increasing temporal indeterminacy of the global ordering. We demonstrate a new method for quantifying this indeterminacy that relies on solving temporal constraint problems to extract timelines, and show that TDTs result in up to a 109{\%} increase in temporal indeterminacy over their corresponding temporal graphs for the three corpora we examine. On average, the increase in indeterminacy is 32{\%}, and we show that this increase is a result of the TDT representation eliminating on average only 2.4{\%} of total temporal relations. This result suggests that small differences can have big effects in temporal graphs, and the use of TDTs must be balanced against their deficiencies, with tasks requiring an accurate global temporal ordering potentially calling for use of the full temporal graph
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Acquiring Structured Temporal Representation via Crowdsourcing: A Feasibility Study
Temporal Dependency Trees are a structured temporal representation that represents temporal relations among time expressions and events in a text as a dependency tree structure. Compared to traditional pair-wise temporal…
Getting More Out Of Syntax with PropS
Semantic NLP applications often rely on dependency trees to recognize major elements of the proposition structure of sentences. Yet, while much semantic structure is indeed expressed by syntax, many phenomena are not eas…
Open Information ExtractionEfficient Parsing for Head-Split Dependency Trees
Head splitting techniques have been successfully exploited to improve the asymptotic runtime of parsing algorithms for projective dependency trees, under the arc-factored model. In this article we extend these techniques…
ARCDependency ParsingImproving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control
Relation Extraction (RE) is one of the fundamental tasks in Information Extraction and Natural Language Processing. Dependency trees have been shown to be a very useful source of information for this task. The current de…
Domain GeneralizationRelationRelation ExtractionGraph Convolution over Pruned Dependency Trees Improves Relation Extraction
Dependency trees help relation extraction models capture long-range relations between words. However, existing dependency-based models either neglect crucial information (e.g., negation) by pruning the dependency trees t…
NegationRelationRelation ClassificationRelation Extraction