Papers by Alon Eirew
Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution (P19-1)
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| Challenge: | Recognizing that various textual spans across multiple texts refer to the same entity or event is an important NLP task. |
| Approach: | They propose a neural architecture for cross-document coreference resolution by representing an event mention using its lexical span, surrounding context, and relation to other mentions via predicate-arguments structures. |
| Outcome: | The proposed model outperforms the state-of-the-art event coreference model on ECB+ while providing the first entity coreference results on this corpus. |
WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia (2021.naacl-main)
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| Challenge: | Existing datasets for cross-document event coreference resolution are limited and small . authors present a method for identifying clusters of text mentions that refer to the same event . |
| Approach: | They propose a method for generating a large-scale Wikipedia event coreference dataset . they use a generic approach that adapts state-of-the-art models to the cross-document setting . |
| Outcome: | The proposed method outperforms existing models and can be applied to other languages. |
Cross-document Event Coreference Search: Task, Dataset and Modeling (2022.emnlp-main)
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| Challenge: | Cross-document Event Coreference resolution is the task of identifying clusters of text mentions that refer to the same event, whether within a single document or across a document collection. |
| Approach: | They propose a cross-document coreference search task that searches for all coreferring mentions for a query event in a large document collection. |
| Outcome: | The proposed model integrates a powerful coreference scoring scheme into the DPR architecture, yielding improved performance. |
Term Set Expansion based NLP Architect by Intel AI Lab (D18-2)
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Jonathan Mamou, Oren Pereg, Moshe Wasserblat, Alon Eirew, Yael Green, Shira Guskin, Peter Izsak, Daniel Korat
| Challenge: | SetExpander is a corpus-based system for expanding a seed set of terms into a more complete set of words belonging to the same semantic class. |
| Approach: | They propose a corpus-based system for expanding a seed set of terms into a more complete set of words that belong to the same semantic class. |
| Outcome: | The proposed system can expand a seed set of terms into a more complete set of words belonging to the same semantic class. |
Cross-document Coreference Resolution over Predicted Mentions (2021.findings-acl)
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| Challenge: | Cross-document coreference resolution has been under-explored in recent years . however, the challenge of cross-document resolution remains relatively under-studied . |
| Approach: | They propose a model for cross-document coreference resolution from raw text that extends a prominent withindocument corefer model to the CD setting. |
| Outcome: | The proposed model achieves competitive results for event and entity coreference resolution on gold mentions. |
Beyond Pairwise: Global Zero-shot Temporal Graph Generation (2025.emnlp-main)
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| Challenge: | Temporal relation extraction (TRE) is a fundamental task in natural language processing (NLP) that involves identifying the temporal relationships between events in a document. |
| Approach: | They propose a method that generates a document’s complete temporal graph in a single step, followed by temporal constraint optimization to refine predictions and enforce temporal consistency across relations. |
| Outcome: | The proposed method outperforms existing zero-shot approaches and offers a competitive alternative to supervised TRE models. |
SetExpander: End-to-end Term Set Expansion Based on Multi-Context Term Embeddings (C18-2)
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Jonathan Mamou, Oren Pereg, Moshe Wasserblat, Ido Dagan, Yoav Goldberg, Alon Eirew, Yael Green, Shira Guskin, Peter Izsak, Daniel Korat
| Challenge: | SetExpander is a corpus-based system for expanding a seed set of terms into a more complete set of words belonging to the same semantic class. |
| Approach: | They propose to use a corpus-based system for expanding a seed set of terms into a more complete set of words that belong to the same semantic class. |
| Outcome: | The proposed system can expand a seed set of terms, validate it, re-expand the expanded set and store it, thus simplifying the extraction of domain-specific fine-grained semantic classes. |
iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration (2021.emnlp-demo)
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Eran Hirsch, Alon Eirew, Ori Shapira, Avi Caciularu, Arie Cattan, Ori Ernst, Ramakanth Pasunuru, Hadar Ronen, Mohit Bansal, Ido Dagan
| Challenge: | iFS provides a faceted navigation scheme that provides abstractive summaries for the user’s selections. |
| Approach: | They propose a web application that integrates interactive summarization and faceted search to provide a faceted navigation scheme that yields abstractive summaries for the user's selections. |
| Outcome: | The proposed system provides a comprehensive overview as well as particular details regard-ing subtopics of interest. |
Realistic Evaluation Principles for Cross-document Coreference Resolution (2021.starsem-1)
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| Challenge: | Using permissive evaluation protocols, cross-document coreference resolution models produce inflated results. |
| Approach: | They propose to decouple evaluation of mention detection from coreference linking . they argue that models should not exploit the synthetic topic structure of the standard ECB+ dataset . |
| Outcome: | The proposed evaluation principles yield lower results than previous lenient evaluation methods. |