223 research outputs found
Novel Event Detection and Classification for Historical Texts
Event processing is an active area of research in the Natural Language Processing community but resources and automatic systems developed so far have mainly addressed contemporary texts. However, the recognition and elaboration of events is a crucial step when dealing with historical texts particularly in the current era of massive digitization of historical sources: research in this domain can lead to the development of methodologies and tools that can assist historians in enhancing their work, while having an impact also on the field of Natural Language Processing. Our work aims at shedding light on the complex concept of events when dealing with historical texts. More specifically, we introduce new annotation guidelines for event mentions and types, categorised into 22 classes. Then, we annotate a historical corpus accordingly, and compare two approaches for automatic event detection and classification following this novel scheme. We believe that this work can foster research in a field of inquiry so far underestimated in the area of Temporal Information Processing. To this end, we release new annotation guidelines, a corpus and new models for automatic annotation
Event Extraction: A Survey
Extracting the reported events from text is one of the key research themes in
natural language processing. This process includes several tasks such as event
detection, argument extraction, role labeling. As one of the most important
topics in natural language processing and natural language understanding, the
applications of event extraction spans across a wide range of domains such as
newswire, biomedical domain, history and humanity, and cyber security. This
report presents a comprehensive survey for event detection from textual
documents. In this report, we provide the task definition, the evaluation
method, as well as the benchmark datasets and a taxonomy of methodologies for
event extraction. We also present our vision of future research direction in
event detection.Comment: 20 page
Semi-Supervised Event Extraction with Paraphrase Clusters
Supervised event extraction systems are limited in their accuracy due to the
lack of available training data. We present a method for self-training event
extraction systems by bootstrapping additional training data. This is done by
taking advantage of the occurrence of multiple mentions of the same event
instances across newswire articles from multiple sources. If our system can
make a highconfidence extraction of some mentions in such a cluster, it can
then acquire diverse training examples by adding the other mentions as well.
Our experiments show significant performance improvements on multiple event
extractors over ACE 2005 and TAC-KBP 2015 datasets.Comment: NAACL 201
CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument Compatibilities
Event coreference resolution (ECR) aims to group event mentions referring to
the same real-world event into clusters. Most previous studies adopt the
"encoding first, then scoring" framework, making the coreference judgment rely
on event encoding. Furthermore, current methods struggle to leverage
human-summarized ECR rules, e.g., coreferential events should have the same
event type, to guide the model. To address these two issues, we propose a
prompt-based approach, CorefPrompt, to transform ECR into a cloze-style MLM
(masked language model) task. This allows for simultaneous event modeling and
coreference discrimination within a single template, with a fully shared
context. In addition, we introduce two auxiliary prompt tasks, event-type
compatibility and argument compatibility, to explicitly demonstrate the
reasoning process of ECR, which helps the model make final predictions.
Experimental results show that our method CorefPrompt performs well in a
state-of-the-art (SOTA) benchmark.Comment: Accepted by EMNLP202
Exploiting the Matching Information in the Support Set for Few Shot Event Classification
The existing event classification (EC) work primarily focuseson the
traditional supervised learning setting in which models are unableto extract
event mentions of new/unseen event types. Few-shot learninghas not been
investigated in this area although it enables EC models toextend their
operation to unobserved event types. To fill in this gap, inthis work, we
investigate event classification under the few-shot learningsetting. We propose
a novel training method for this problem that exten-sively exploit the support
set during the training process of a few-shotlearning model. In particular, in
addition to matching the query exam-ple with those in the support set for
training, we seek to further matchthe examples within the support set
themselves. This method providesmore training signals for the models and can be
applied to every metric-learning-based few-shot learning methods. Our extensive
experiments ontwo benchmark EC datasets show that the proposed method can
improvethe best reported few-shot learning models by up to 10% on accuracyfor
event classificationComment: Pacific-Asia Conference on Knowledge Discovery and Data Mining
(PAKDD) 202
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