Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource (N18-1)
Copied to clipboard
| Challenge: | Existing temporal extraction systems that extract temporal relations can be improved by using a resource that provides prior knowledge of the temporal order that events usually follow. |
| Approach: | They propose to use a probabilistic knowledge base acquired in the news domain to extract temporal relations between events from the New York Times articles over a 20-year span. |
| Outcome: | The proposed system and resource are both publicly available. |
Similar Papers
Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to extract event temporal relations from text data are limited by hard constraints and large datasets. |
| Approach: | They propose a framework that enhances deep neural network with distributional constraints constructed by probabilistic domain knowledge to improve the baseline neural network models. |
| Outcome: | The proposed framework improves baseline models with strong statistical significance on two widely used datasets in news and clinical domains. |
Fine-Grained Temporal Relation Extraction (P19-1)
Copied to clipboard
| Challenge: | Existing methods for temporal relations and event durations are insufficient for determining the fine-grained temporal structure of complex events. |
| Approach: | They propose a semantic framework for temporal relations and event durations that maps pairs of events to real-valued scales. |
| Outcome: | The proposed framework can predict fine-grained temporal relations and event durations . it can be applied to the entire English Web Treebank dataset . |
Temporal Information Extraction by Predicting Relative Time-lines (D18-1)
Copied to clipboard
| Challenge: | a new paradigm for temporal information extraction from text evades the relation extraction phase because there are n 2 possible entity pairs in a text with n temporal entities. |
| Approach: | They propose a method to construct a linear time-line from a set of temporal relations from text without the intermediate step of prediction of tempor relations. |
| Outcome: | The proposed method predicts start and end-points without intermediate step of prediction of temporal relations . it evades phase 2 because there are n 2 possible entity pairs in the extraction phase . |
TIMELINE: Exhaustive Annotation of Temporal Relations Supporting the Automatic Ordering of Events in News Articles (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing temporal relation extraction models have low inter-annotator agreement due to lack of specificity of annotation guidelines . authors propose a method for annotating all temporal relations, including long-distance ones, which automates the process . |
| Approach: | They propose a new annotation scheme that defines criteria for temporal relations to be annotated . scheme includes events even if they are not expressed as verbs, they argue . |
| Outcome: | The proposed method reduces time and manual effort on the part of annotators. |
An Improved Neural Baseline for Temporal Relation Extraction (D19-1)
Copied to clipboard
| Challenge: | Existing datasets are small and/or have low inter-annotator agreements. |
| Approach: | They propose a new neural system that achieves 10% absolute accuracy improvement over the previous best system. |
| Outcome: | The proposed system achieves 10% absolute improvement over the previous best system on two benchmark datasets. |
Consistent Discourse-level Temporal Relation Extraction Using Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have spurred research on temporal relation extraction tasks. |
| Approach: | They propose a framework to improve LLMs’ temporal relation extraction capabilities using context selection, prompts inspired by Allen’s interval algebra and reflection-based consistency learning. |
| Outcome: | The proposed framework improves LLMs’ extraction capabilities by focusing on context selection, prompts inspired by Allen’s interval algebra and reflection-based consistency learning. |
Event Temporal Relation Extraction with Bayesian Translational Model (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing methods to extract temporal relations between events lack a principled method to incorporate external knowledge. |
| Approach: | They propose a Bayesian-based method that models the temporal relation representations as latent variables and infers their values via Bayessian inference and translational functions. |
| Outcome: | The proposed method outperforms existing methods for event temporal relation extraction on three widely used datasets. |
Relation Extraction with Temporal Reasoning Based on Memory Augmented Distant Supervision (N19-1)
Copied to clipboard
| Challenge: | Distant supervision is an important paradigm for automatically extracting relations . but the examples collected can be noisy and pose significant challenge for labeling . |
| Approach: | They propose a method to predict whether two entities participate in a relation at a given time spot. |
| Outcome: | The proposed model performs better in WIKI-TIME and NYT-10 datasets compared with the best existing models . the proposed model is based on a dataset with a valid period of a certain relation of two entities in the knowledge base . |
DCT-Centered Temporal Relation Extraction (2022.coling-1)
Copied to clipboard
| Challenge: | Existing work on temporal relation extraction focuses on extracting temporal relations between events . previous work on relation extraction focused on focusing on event-centered tasks . |
| Approach: | They propose a temporal relation extraction model that unifies events, timexes and DCT . they propose combining event mentions, time expressions and document creation time into a sentence-style model . |
| Outcome: | The proposed model outperforms baselines on E-E, E-T and E-D significantly. |
Best of Both Worlds: Towards Improving Temporal Knowledge Base Question Answering via Targeted Fact Extraction (2023.emnlp-main)
Copied to clipboard
Nithish Kannen, Udit Sharma, Sumit Neelam, Dinesh Khandelwal, Shajith Ikbal, Hima Karanam, L Subramaniam
| Challenge: | Temporal question answering (QA) is a complex task that requires reasoning over facts asserting time intervals of events. |
| Approach: | They propose a temporal fact extraction technique that helps QA when it fails to retrieve temporal facts from the KB. |
| Outcome: | The proposed technique can extract temporal facts that failed to get retrieved from the KB without additional training cost. |