Unsupervised Event Clustering and Aggregation from Newswire and Web Articles

Ribeiro, Swen; Ferret, Olivier; Tannier, Xavier

doi:10.18653/v1/w17-4211

Cited by 13 publications

(9 citation statements)

References 7 publications

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“…A great portion of works focuses on detecting keywords to cluster sentences/articles expressing the same event or discussing a target topic in a text stream (i.e., topic tracking) [117]- [120], [127]- [132]. Documents are typically first converted into vectorial representations (e.g., BOW [131], TF-IDF [127], [130], lexical chains [128], multinomial distributions via Bayesian approaches [117]- [119], [129] or GANs [120]), sometimes augmented with named entities, time, and location [117], [119], [120], [129], [131], [132]. Each resulting cluster (e.g., k-means [132], agglomerative hierarchical clustering [127], sequence-based and iterative TF-IDF clustering [130], Markov clustering [131]) brings together documents describing the same high-level event (termed as meta-event), whose simplified definition depends on the previously modeled and embedded information (i.e, semantically similar words, temporal proximity, etc.…”

Section: ) Unsupervised Learningmentioning

confidence: 99%

A Survey on Event Extraction for Natural Language Understanding: Riding the Biomedical Literature Wave

Frisoni¹,

Moro

Carbonaro

2021

IEEE Access

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Motivation: The scientific literature embeds an enormous amount of relational knowledge, encompassing interactions between biomedical entities, like proteins, drugs, and symptoms. To cope with the everincreasing number of publications, researchers are experiencing a surge of interest in extracting valuable, structured, concise, and unambiguous information from plain texts. With the development of deep learning, the granularity of information extraction is evolving from entities and pairwise relations to events. Events can model complex interactions involving multiple participants having a specific semantic role, also handling nested and overlapping definitions. After being studied for years, automatic event extraction is on the road to significantly impact biology in a wide range of applications, from knowledge base enrichment to the formulation of new research hypotheses. Results: This paper provides a comprehensive and up-to-date survey on the link between event extraction and natural language understanding, focusing on the biomedical domain. First, we establish a flexible event definition, summarizing the terminological efforts conducted in various areas. Second, we present the event extraction task, the related challenges, and the available annotated corpora. Third, we deeply explore the most representative methods and present an analysis of the current state-of-the-art, accompanied by performance discussion. To help researchers navigate the avalanche of event extraction works, we provide a detailed taxonomy for classifying the contributions proposed by the community. Fourth, we compare solutions applied in biomedicine with those evaluated in other domains, identifying research opportunities and providing insights for strategies not yet explored. Finally, we discuss applications and our envisions about future perspectives, moving the needle on explainability and knowledge injection.INDEX TERMS Biomedical text mining, event extraction, natural language understanding, semantic parsing.

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Section: ) Unsupervised Learningmentioning

confidence: 99%

A Survey on Event Extraction for Natural Language Understanding: Riding the Biomedical Literature Wave

Frisoni¹,

Moro

Carbonaro

2021

IEEE Access

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“…It is based on simulating random walks along nodes in a graph. Ribeiro et al (2017) use this approach for clustering news articles.…”

Section: Clusteringmentioning

confidence: 99%

Examining the State-of-the-Art in News Timeline Summarization

Ghalandari¹,

Ifrim²

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Previous work on automatic news timeline summarization (TLS) leaves an unclear picture about how this task can generally be approached and how well it is currently solved. This is mostly due to the focus on individual subtasks, such as date selection and date summarization, and to the previous lack of appropriate evaluation metrics for the full TLS task. In this paper, we compare different TLS strategies using appropriate evaluation frameworks, and propose a simple and effective combination of methods that improves over the stateof-the-art on all tested benchmarks. For a more robust evaluation, we also present a new TLS dataset, which is larger and spans longer time periods than previous datasets. The dataset will be made available at https://github. com/complementizer/news-tls.

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“…Following the task definition of the TDT program, some other researches have been conducted to detect whether new articles in various websites are related to some already identified event, without using the TDT corpus [32]- [37], [238]. For example, Naughton et al [35] proposed to vectorize sentences using the bag-of-words encoding from news articles and clustered sentences via using the agglomerative hierarchical clustering algorithm [239].…”

Section: A Event Mention Detection and Trackingmentioning

confidence: 99%

“…Besides using word and sentence embeddings, some have proposed to exploit several additional information of news articles, like time and location, to augment event mention detection. Ribeiro et al [32] proposed to integrate time, location and content dimensions into text representation, and applied an all pairs similarity search algorithm and a Markov clustering algorithm to cluster news articles describing the same event. Likewise, Yu and Wu [33] proposed to a Time2Vec representation technique that constructs article representation by a context vector and time vector and employed a dual-level clustering algorithm for event detection.…”

Section: A Event Mention Detection and Trackingmentioning

confidence: 99%

A Survey of Event Extraction From Text

2019

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Numerous important events happen everyday and everywhere but are reported in different media sources with different narrative styles. How to detect whether real-world events have been reported in articles and posts is one of the main tasks of event extraction. Other tasks include extracting event arguments and identifying their roles, as well as clustering and tracking similar events from different texts. As one of the most important research themes in natural language processing and understanding, event extraction has a wide range of applications in diverse domains and has been intensively researched for decades. This article provides a comprehensive yet up-to-date survey for event extraction from text. We not only summarize the task definitions, data sources and performance evaluations for event extraction, but also provide a taxonomy for its solution approaches. In each solution group, we provide detailed analysis for the most representative methods, especially their origins, basics, strengths and weaknesses. Last, we also present our envisions about future research directions. INDEX TERMS Event extraction, event extraction tasks, event corpus, natural language processing.

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Unsupervised Event Clustering and Aggregation from Newswire and Web Articles

Cited by 13 publications

References 7 publications

A Survey on Event Extraction for Natural Language Understanding: Riding the Biomedical Literature Wave

A Survey on Event Extraction for Natural Language Understanding: Riding the Biomedical Literature Wave

Examining the State-of-the-Art in News Timeline Summarization

A Survey of Event Extraction From Text

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