Sentence Level Temporality Detection using an Implicit Time-sensed Resource (L18-1)
Copied to clipboard
| Challenge: | Temporal sense detection of any word is an important aspect for detecting temporality at the sentence level. |
| Approach: | They build a temporal resource based on a semi-supervised learning approach . they use past, present, future, neutral and atemporal senses to tag sentences . |
| Outcome: | The proposed resource is based on a semi-supervised learning approach . it is used to tag sentences with past, present and future temporal senses . |
Similar Papers
Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models (P19-1)
Copied to clipboard
| Challenge: | Recent studies show that document classifiers can become more stable over time when trained in ways that account for temporal variations. |
| Approach: | They propose a method for embedding diachronic word embedds into document classification models . they propose 'time-driven neural classification model' that accounts for temporal variations . |
| Outcome: | The proposed model can be trained on six corpora and make it more robust over time. |
Uncovering Temporal Framing in the News (2026.acl-long)
Copied to clipboard
Tarek Mahmoud, Veronika Solopova, Premtim Sahitaj, Ariana Sahitaj, Max Upravitelev, Mervat Abassy, Hana Fatima Shaikh, Neda Foroutan, Vera Schmitt, Preslav Nakov
| Challenge: | Temporal language is used to structure meaning rather than report chronology in news discourse . a recent study focused on temporal expression extraction and temporal reasoning . |
| Approach: | They propose a taxonomy of eight temporal frames grounded in prior work on time and framing . they analyze frame prevalence, co-occurrence patterns, and lexical cues from a news corpus . |
| Outcome: | The proposed taxonomy outperforms zero-shot models at the sentence level . it shows that temporal framing is learnable at the sentences level compared to other methods . |
Learning to Focus on the Foreground for Temporal Sentence Grounding (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for temporal sentence grounding do not capture subtle details of small objects. |
| Approach: | They propose a detection-free framework for temporal sentence grounding that learns to locate foreground regions related to the query in consecutive frames. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three challenging datasets. |
TIMERS: Document-level Temporal Relation Extraction (2021.acl-short)
Copied to clipboard
| Challenge: | Existing methods for temporal relation extraction focus on extracting temporal relations between event pairs present in the same sentence or adjacent sentences, mostly ignoring document-level pairs. |
| Approach: | They propose a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language that leverages rhetorical discourse features and temporal arguments from semantic role labels. |
| Outcome: | The proposed model outperforms previous methods on the TDDiscourse, TimeBank-Dense, and MATRES datasets due to its discourse-level modeling. |
Modeling Document-level Temporal Structures for Building Temporal Dependency Graphs (2022.aacl-short)
Copied to clipboard
| Challenge: | Using news discourse profiling, we can identify temporal relationships between events and time expressions that are temporally related and otherwise difficult to locate. |
| Approach: | They propose to leverage news discourse profiling to model document-level temporal structures for building temporal dependency graphs. |
| Outcome: | The proposed model can identify distant inter-sentence event and (or) time expression pairs that are temporally related and otherwise difficult to locate. |
Timeline-based Sentence Decomposition with In Context Learning for Temporal Fact Extraction (2024.acl-long)
Copied to clipboard
| Challenge: | Recent research on temporal fact extraction fails to establish time-to-fact correspondences in complex sentences. |
| Approach: | They propose a timeline-based sentence decomposition strategy using large language models with in-context learning to extract temporal facts from natural language text. |
| Outcome: | The proposed method achieves state-of-the-art on a complex temporal fact extraction dataset. |
Diachronic Sense Modeling with Deep Contextualized Word Embeddings: An Ecological View (P19-1)
Copied to clipboard
| Challenge: | Existing word embeddings only assign one vector to a word for a time period, thus they face the meaning conflation deficiency. |
| Approach: | They propose a sense representation and tracking framework based on deep contextualized embeddings that can be used to answer what and when the word meaning changes. |
| Outcome: | The proposed framework is effective in representing fine-grained word senses, and brings a significant improvement in word change detection task. |
Exploring Contextualized Neural Language Models for Temporal Dependency Parsing (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent work shows that deep contextualized language models (LMs) can extract temporal relations between events and time expressions. |
| Approach: | They propose a temporal relation extraction technique which extracts temporal relations between events and time expressions. |
| Outcome: | The proposed method significantly improves temporal dependency parsing, the authors show . their work compares the proposed method to other methods and shows where they may fail . |
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. |
Examining Temporality in Document Classification (P18-2)
Copied to clipboard
| Challenge: | a recent study examines how document classification models trained during one time period perform on documents trained during other time periods. |
| Approach: | They propose to use a domain adaptation approach to adjust for changes in time to improve document classification. |
| Outcome: | The proposed model improves on documents trained on time intervals even on future time interval intervals. |