Challenge: Existing information extraction systems are not able to accurately capture organizational changes.
Approach: They propose a task to extract corporate history events related to organizational changes by identifying company names before and after each event, as well as the corresponding date.
Outcome: The proposed task is designed to identify company names before and after an event, as well as the corresponding date.

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Financial Event Extraction Using Wikipedia-Based Weak Supervision (D19-51)

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Challenge: Existing methods for detecting financial and economic events from text have relied on a knowledge-base of financial events, or corresponding financial figures.
Approach: They propose to use Wikipedia sections to extract weak labels for sentences describing economic events from text.
Outcome: The proposed method can extract weak labels for sentences describing economic events from Wikipedia sentences.
Event Extraction from Historical Texts: A New Dataset for Black Rebellions (2021.findings-acl)

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Challenge: Using EE to extract historical data, we study the discourse of slave and non-slave African diaspora rebellions published in the periodical press in this period.
Approach: They propose a dataset to detect event trigger words and their arguments in nineteenth-century newspapers.
Outcome: The proposed dataset features 5 entity types, 12 event types, and 6 argument roles that concern slavery and black movements between the eighteenth and nineteenth centuries.
Cross-lingual Structure Transfer for Relation and Event Extraction (D19-1)

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Challenge: Existing approaches to identify complex semantic structures are difficult to train from under-annotated sources.
Approach: They exploit relation- and event-relevant language-universal features to train relation or event extractors from source annotations and apply them to target languages.
Outcome: The proposed approach achieves comparable performance to state-of-the-art models trained on 3,000 manually annotated mentions.
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.
Approach: This tutorial will provide an introduction to various methods for automating events and their relations, and a wide range of NLU and commonsense understanding tasks.
Outcome: This tutorial will provide an introduction to various methods for automating extraction, conceptualization and prediction of events and their relations, and a wide range of NLU and commonsense understanding tasks.
Letters From the Past: Modeling Historical Sound Change Through Diachronic Character Embeddings (2022.acl-long)

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Challenge: a great deal of work has been done on NLP approaches to lexical semantic change detection, but other aspects of language change have received less attention from the NLP community.
Approach: They propose to compare the relative distance through time between the distributions of the characters involved before and after a sound change has taken place.
Outcome: The proposed method can trace the well-known historical change of lenition of plosives in Danish historical sources and identify several of the changes under consideration and uncover meaningful contexts in which they appeared.
TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction (2024.findings-acl)

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Challenge: Recent studies suggest that event extraction evaluations may not accurately reflect the true performance.
Approach: They propose a standardized, fair, and reproducible benchmark for event extraction . they use standardized scripts and splits for 16 datasets spanning eight domains .
Outcome: The proposed benchmarks show that they struggle to achieve satisfactory performance.
Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading (2021.findings-acl)

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Challenge: Existing models that use textual features and sentiments to make stock predictions are poor explainability and low signal-to-noise ratio.
Approach: They propose a bi-level event detection model that detects corporate events from news articles and an elaborately-annotated dataset EDT for corporate event detection and news-based stock prediction benchmark.
Outcome: The proposed strategy outperforms baselines in winning rate, excess returns over the market, and the average return on each transaction.
Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents (2021.eacl-main)

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Challenge: Existing methods for event reason extraction are far from resolving this problem.
Approach: They propose a task to extract causal explanations from document-level texts . they use a dataset FinReason for evaluation to provide Reasons annotation for financial events .
Outcome: The proposed task performs better than existing methods on a dataset of 8,794 documents, 12,861 financial events and 11,006 reason spans.
Detecting Syntactic Change with Pre-trained Transformer Models (2023.findings-emnlp)

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Challenge: a fine-tuned BERT model can distinguish between text from the early 1800s and late 1900s . we use it to identify specific instances of syntactic change and specific words for which a new part of speech was introduced.
Approach: They propose to use a BERT-based model to find syntactic differences between English of the early 1800s and that of the late 1900s.
Outcome: The proposed model can distinguish between English of the early 1800s and that of the late 1900s using only syntactic information.
Cross-Domain Evaluation of Edge Detection for Biomedical Event Extraction (2020.lrec-1)

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Challenge: Biomedical event extraction systems are evaluated in-domain and on complete event structures only.
Approach: They present a cross-domain study of edge detection for biomedical event extraction . they analyze differences between five existing gold standard corpora and provide a strong baseline model .
Outcome: The proposed model shows a drop in performance when the baseline is applied on out-of-domain data.

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