| Challenge: | Initial experiments on standard NER task due to complexity of dataset and rich NE annotation scheme are promising with respect to some labels and give insights on handling better other ones. |
| Approach: | They describe a Bulgarian Event Corpus (BEC) that includes named entities and events with their roles. |
| Outcome: | The proposed corpus is multi-domain and oriented towards Social Sciences and Humanities (SSH) it includes named entities and events with their roles. |
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| Challenge: | Named Entity Recognition (NER) and Named Enel Linking (NEL) are two related tasks that are under-resourced for the Slavic languages. |
| Approach: | They propose to use deep learning methods to improve a Named Entity Recognition corpus and to predict and annotate new types in a test corpus. |
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Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence (2021.findings-acl)
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| Challenge: | a new corpus-level evaluation approach for event extraction is needed in social science applications . human annotations are often required to extract the actions of political actors and actors . a novel corpus evaluation approach can guide creation of similar social science-oriented resources . |
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Natural Language Processing Pipeline to Annotate Bulgarian Legislative Documents (2020.lrec-1)
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| Challenge: | The Bulgarian MARCELL corpus consists of 25,283 documents, which are classified into eleven types. |
| Approach: | They present the Bulgarian MARCELL corpus, part of a newly developed multilingual corpus representing the national legislation in seven European countries. |
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Spanless Event Annotation for Corpus-Wide Complex Event Understanding (2024.lrec-main)
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| Challenge: | Existing methods for annotating multilingual, multimedia data are limited by the availability of multilingual corpora for schema-based event representation. |
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Schema Learning Corpus: Data and Annotation Focused on Complex Events (2024.lrec-main)
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| Challenge: | The Schema Learning Corpus is a linguistic resource designed to support research into the structure of complex events in multilingual data. |
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UkraiNER: A New Corpus and Annotation Scheme towards Comprehensive Entity Recognition (2024.lrec-main)
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| Challenge: | Named entity recognition excludes nested, discontinuous, non-named entities in practice . despite attempts to broaden their coverage, the most restrictive variant of NER remains the default . |
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Event-Centric Natural Language Processing (2021.acl-tutorials)
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| Challenge: | This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations. |
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Manovaad: A Novel Approach to Event Oriented Corpus Creation Capturing Subjectivity and Focus (2020.lrec-1)
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| Challenge: | Several studies conducted on the different styles of reporting in journalism are essential in understanding phenomena such as media bias and multiple interpretations of the same event. |
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Corpus Creation and Analysis for Named Entity Recognition in Telugu-English Code-Mixed Social Media Data (P19-2)
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| Challenge: | Named Entity Recognition (NER) is a subtask of Information Extraction in NLP. |
| Approach: | They present a Telugu-English code-mixed corpus with the corresponding named entity tags. |
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FAMuS: Frames Across Multiple Sources (2024.naacl-long)
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| Challenge: | Recent work in document-level event and argument extraction tasks suffer from two key shortcomings. |
| Approach: | They propose to combine Wikipedia passages with underlying, genre-diverse source articles for an event . they propose two key task enabled by FAMuS: source validation and cross-document argument extraction . |
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