Papers with document clustering
Okay, Let’s Do This! Modeling Event Coreference with Generated Rationales and Knowledge Distillation (2024.naacl-long)
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| Challenge: | Recent work shows that generative large language models (LLMs) can be used to solve cross-document coreference problems. |
| Approach: | They propose rationale-oriented event clustering and knowledge distillation methods for event coreference scoring that leverage enriched information from the FTRs for improved CDCR. |
| Outcome: | The proposed model achieves SOTA B3 F1 on the ECB+ and GVC corpora without additional annotation or expensive document clustering. |
Semantic Relatedness Based Re-ranker for Text Spotting (D19-1)
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| Challenge: | Existing approaches to text spotting are limited by semantic similarity, but they can be useful for other tasks. |
| Approach: | They propose a neural approach to learn semantic relatedness from existing sentences. |
| Outcome: | The proposed approach outperforms existing approaches when applied to a text spotting task. |
Autoencoding Keyword Correlation Graph for Document Clustering (2020.acl-main)
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| Challenge: | Existing representation learning models do not capture the intra-sentential and inter-sententential features of long-text. |
| Approach: | They propose a graph-based representation for document clustering that builds a Graph Autoencoder on a Keyword Correlation Graph. |
| Outcome: | The proposed graph autoencoder can achieve better clustering performance than existing features. |
Leveraging Meta Information in Short Text Aggregation (P19-1)
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| Challenge: | Existing topic models infer topics based on word co-occurrence information, which results in degraded performance and degrades performance. |
| Approach: | They propose a generative model that aggregates short texts into clusters by leveraging the associated meta information. |
| Outcome: | The proposed model can generate more interpretable topics and document clusters. |
TacoERE: Cluster-aware Compression for Event Relation Extraction (2024.lrec-main)
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| Challenge: | Existing work on event relation extraction focuses on modeling the entire document . existing methods cannot handle long-range dependencies and information redundancy . |
| Approach: | They propose a compression-then-extraction paradigm for event relation extraction . they propose document clustering for modeling event dependencies and then a cluster summarization method . |
| Outcome: | The proposed method simplifies and highlights important text content of clusters for mitigating redundancy and event distance. |