Topic Detection and Tracking with Time-Aware Document Embeddings (2024.lrec-main)
Copied to clipboard
| Challenge: | Topic Detection and Tracking (TDT) systems aim to cluster news articles into real-world events. |
| Approach: | They propose a neural method that fuses temporal and textual information into a single representation of news documents for event detection. |
| Outcome: | The proposed model outperforms baselines on two benchmark TDT data sets in English. |
Similar Papers
Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event (2021.acl-short)
Copied to clipboard
| Challenge: | Current approaches focus on news articles and expect at least one temporal expressions in each input data to predict TTE. |
| Approach: | They propose a context-sensitive neural model for time to event prediction task . they enrich the model with time difference embeddings to improve accuracy . |
| Outcome: | The proposed model is 1.4 and 3.3 hours more accurate than the current state-of-the-art model on English and Dutch tweets respectively. |
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. |
ECOLA: Enhancing Temporal Knowledge Embeddings with Contextualized Language Representations (2023.findings-acl)
Copied to clipboard
Zhen Han, Ruotong Liao, Jindong Gu, Yao Zhang, Zifeng Ding, Yujia Gu, Heinz Koeppl, Hinrich Schütze, Volker Tresp
| Challenge: | Existing enhancement approaches cannot be applied to temporal knowledge graphs (tKGs) existing enhancement approaches assume knowledge embedding is time-independent, whereas entity embedded in tKG models evolves . |
| Approach: | They propose to use textual data to enhance temporal knowledge embedding by Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations (ECOLA) to evaluate ECOLA, they introduce three new datasets for training and evaluation. |
| Outcome: | The proposed model significantly improves Hits@1 on the link prediction task. |
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. |
Diachronic word embeddings and semantic shifts: a survey (C18-1)
Copied to clipboard
| Challenge: | Existing methods for tracing time-related semantic shifts with word embedding models lack the cohesion, common terminology and shared practices of more established areas of natural language processing. |
| Approach: | They propose several axes along which these methods can be compared and propose a framework for comparison. |
| Outcome: | The proposed methods are compared with existing methods and outline their main challenges and potential applications. |
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. |
Dynamic Topic Tracker for KB-to-Text Generation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing KB-to-text generation models suffer from an off-topic problem . existing models generate unrelated clauses regardless of input data . |
| Approach: | They propose a dynamic topic tracker that learns a global hidden representation for topics and recognizes the corresponding topic during each generation step. |
| Outcome: | The proposed model improves the performance of sentence generation and mitigates off-topic problem. |
Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings (2021.eacl-main)
Copied to clipboard
| Challenge: | a novel method for online news stream clustering is proposed . a user can scour the many news sources multiple times a day to find news articles . |
| Approach: | They propose a method for online news stream clustering that is a variant of the streaming K-means algorithm. |
| Outcome: | The proposed model achieves state-of-the-art on a standard stream clustering dataset of English documents. |
Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing methods for word embeddings have been used to model semantic relations with word embeds. |
| Approach: | They propose a method that leverages contextual embeddings for diachronic semantic shift detection by generating time specific word representations from BERT embedds. |
| Outcome: | The proposed method performs comparable to the current state-of-the-art without time consuming domain adaptation on large corpora. |
Event Time Extraction and Propagation via Graph Attention Networks (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing work on grounding events into a precise timeline has been limited due to the inherent ambiguity of language and the requirement for information propagation over inter-related events. |
| Approach: | They propose a 4-tuple temporal representation for entity slot filling to ground events into a timeline using a graph attention network approach. |
| Outcome: | The proposed approach yields 7.0% match rate over contextualized embedding approaches and 16.3% higher match rate compared to sentence-level manual event time argument annotation. |