Event Detection

We present a general framework for automatically extracting social networks and biographical facts from conversational speech. Our approach relies on fusing the output produced by multiple informa- tion extraction modules, …...

The ability to correctly classify sentences that describe events is an important task for many natural language applications such as Question Answering (QA) and Sum- marisation. In this paper, we …...

AbstractIn recent years we have resgitered a renewed interest in event detection and temporal processing of text/discourse. TimeML (Pustejovsky et al., 2003a) has shed new lights on the notion of …...

With the recent rise in popularity and size of social media, there is a growing need for sys- tems that can extract useful information from this amount of data. We …...

AbstractTimeBank (Pustejovsky et al, 2003a), a reference for TimeML (Pustejovsky et al, 2003b) compliant annotation, is widely used temporally annotated corpus in the community. It captures time expressions, events, and …...

Event Detection (ED), one aspect of In- formation Extraction, involves identifying instances of specified types of events in text. Much of the research on ED has been based on the …...

Event Detection (ED) is an Information Extraction task which involves identifying instances of specified types of events in text. Most recent research on Event De- tection relies on pattern-based or …...

This paper presents a multilingual corpus of news, annotated with event metadata in- formation. The events in our corpus are from the domain of violence, natural and man made disasters. …...

Frames defined in FrameNet (FN) share highly similar structures with events in ACE event extraction program. An even- t in ACE is composed of an event trig- ger and a …...

AbstractStreaming media provides a number of unique challenges for computational linguistics. This paper studies the temporal variation in word co-occurrence statistics, with application to event detection. We develop a spectral …...

This paper describes VUACLTL, the system the CLTL Lab submitted to the SemEval 2016 Task Clinical TempEval. The system is based on a purely data-driven approach based on a cascade …...

AbstractThis paper tackles the task of event detection (ED), which involves identifying and categorizing events. We argue that arguments provide significant clues to this task, but they are either completely …...

AbstractDetecting which tweets describe a specific event and clustering them is one of the main challenging tasks related to Social Media currently addressed in the NLP community. Existing approaches have …...

AbstractIn this paper, we describe the system of the KULeuven-LIIR submission for Clinical TempEval 2017. We participated in all six subtasks, using a combination of Support Vector Machines (SVM) for …...

AbstractIn supervised event detection, most of the mislabeling occurs between a small number of confusing type pairs, including trigger-NIL pairs and sibling sub-types of the same coarse type. To address …...

AbstractEvent detection systems rely on discrimination knowledge to distinguish ambiguous trigger words and generalization knowledge to detect unseen/sparse trigger words. Current neural event detection approaches focus on trigger-centric representations, which …...

AbstractSyntactic relations are broadly used in many NLP tasks. For event detection, syntactic relation representations based on dependency tree can better capture the interrelations between candidate trigger words and related …...

AbstractThe goal of event detection (ED) is to detect the occurrences of events and categorize them. Previous work solved this task by recognizing and classifying event triggers, which is defined …...

AbstractWe present Event2018, a corpus annotated for event detection tasks, consisting of 38 million tweets in French (retweets excluded) including more than 130,000 tweets manually annotated by three annotators as …...

AbstractThe task of Entity linking, which aims at associating an entity mention with a unique entity in a knowledge base (KB), is useful for advanced Information Extraction tasks such as …...

AbstractEvent detection (ED), a key subtask of information extraction, aims to recognize instances of specific event types in text. Previous studies on the task have verified the effectiveness of integrating …...

AbstractRecent studies on event detection (ED) have shown that the syntactic dependency graph can be employed in graph convolution neural networks (GCN) to achieve state-of-the-art performance. However, the computation of …...

AbstractThe rise of social media platforms makes it a valuable information source of recent events and users’ perspective towards them. Twitter has been one of the most important communication platforms …...

AbstractEvent detection (ED) aims to identify and classify event triggers in texts, which is a crucial subtask of event extraction (EE). Despite many advances in ED, the existing studies are …...

AbstractEvent Detection (ED) is a fundamental task in automatically structuring texts. Due to the small scale of training data, previous methods perform poorly on unseen/sparsely labeled trigger words and are …...

AbstractDetecting cybersecurity events is necessary to keep us informed about the fast growing number of such events reported in text. In this work, we focus on the task of event …...

AbstractMost existing work on event extraction (EE) either follows a pipelined manner or uses a joint structure but is pipelined in essence. As a result, these efforts fail to utilize …...

AbstractEvent detection (ED), which means identifying event trigger words and classifying event types, is the first and most fundamental step for extracting event knowledge from plain text. Most existing datasets …...

AbstractActive learning is an important technique for low-resource sequence labeling tasks. However, current active sequence labeling methods use the queried samples alone in each iteration, which is an inefficient way …...

Event detection (ED) aims at detecting event trigger words in sentences and classifying them into specific event types. In real-world applications, ED typically does not have suf- ficient labelled data, …...

AbstractSpan extraction, aiming to extract text spans (such as words or phrases) from plain text, is a fundamental process in Information Extraction. Recent works introduce the label knowledge to enhance …...

Event detection is a fundamental task in infor- mation extraction. Most previous approaches typically view event detection as a trigger- based classification problem, focusing on us- ing syntactic dependency structure …...

AbstractA core task in information extraction is event detection that identifies event triggers in sentences that are typically classified into event types. In this study an event is considered as …...

AbstractMost of the previous work on Event Detection (ED) has only considered the datasets with a small number of event types (i.e., up to 38 types). In this work, we …...

AbstractTraditional supervised Information Extraction (IE) methods can extract structured knowledge elements from unstructured data, but it is limited to a pre-defined target ontology. In reality, the ontology of interest may …...

AbstractThe task of Event Detection (ED) in Information Extraction aims to recognize and classify trigger words of events in text. The recent progress has featured advanced transformer-based language models (e.g., …...

AbstractSlow emerging topic detection is a task between event detection, where we aggregate behaviors of different words on short period of time, and language evolution, where we monitor their long …...

AbstractEvent Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most current methods to ED rely heavily on training instances, …...

AbstractEvent Detection (ED) aims to recognize mentions of events (i.e., event triggers) and their types in text. Recently, several ED datasets in various domains have been proposed. However, the major …...

Due to the multi-dimensional variation of tex- tual data, detection of event triggers from new domains can become a lot more challenging. This prompts a need to research on domain …...

AbstractMany recent sentence-level event detection efforts focus on enriching sentence semantics, e.g., via multi-task or prompt-based learning. Despite the promising performance, these methods commonly depend on label-extensive manual annotations or …...

AbstractEvent detection, which aims to identify instances of specific event types from pieces of text, is a fundamental task in information extraction. Most existing approaches leverage syntactic knowledge with a …...

AbstractPrevious studies on the timeline summarization (TLS) task ignored the information interaction between sentences and dates, and adopted pre-defined unlearnable representations for them. They also considered date selection and event …...

AbstractEvent detection aims to detect events from the text by identifying and classifying event triggers (the most representative words). Most of the existing works rely heavily on complex downstream networks …...

AbstractEvent Detection (ED) is the task of identifying and classifying trigger words of event mentions in text. Despite considerable research efforts in recent years for English text, the task of …...

AbstractEvent detection (ED) identifies and classifies event triggers from unstructured texts, serving as a fundamental task for information extraction. Despite the remarkable progress achieved in the past several years, most …...

AbstractThe paper describes the Bulgarian Event Corpus (BEC). The annotation scheme is based on CIDOC-CRM ontology and on the English Framenet, adjusted for our task. It includes two main layers: …...

AbstractEvent Detection (ED) aims to identify mentions/triggers of real world events in text. In the literature, this task is modeled as a sequence-labeling or word-prediction problem. In this work, we …...

AbstractThe event extraction task typically consists of event detection and event argument extraction. Most previous work models these two subtasks with shared representation by multiple classification tasks or a unified …...

Abstract“Event detection (ED) is a crucial area of natural language processing that automates the extrac-tion of specific event types from large-scale text, and studying historical ED in classical Chinesetexts helps …...

AbstractMost event detection methods act at the sentence-level and focus on identifying sentences related to a particular event. However, identifying certain parts of a sentence that act as event triggers …...

AbstractIn this paper, we address the Event Detection task under a zero-shot cross-lingual setting where a model is trained on a source language but evaluated on a distinct target language …...

AbstractEvent detection (ED) seeks to discover and classify event instances in plain texts. Previous methods for ED typically adopt supervised learning, requiring fully labeled and high-quality training data. However, in …...

AbstractWith emerging online topics as a source for numerous new events, detecting unseen / rare event types presents an elusive challenge for existing event detection methods, where only limited data …...