On Emerging Entity Detection

Färber, Michael; Rettinger, Achim; Asmar, Boulos

doi:10.1007/978-3-319-49004-5_15

Cited by 14 publications

(16 citation statements)

References 8 publications

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“…It also found homographic entities (e.g., NEVER LAND (music album), Summer of Love (musical movie)), which were not found with Baselines. Although it has been reported that these homographic emerging entities are difficult to find [Hoffart et al, 2014;Färber et al, 2016], our method successfully discovered these entities by obtaining the emerging contexts of the entities. As the evaluation of relative recall, we focused on the best-performing method, i.e., Proposed (LSTM-CRF) and computed its relative recall over the reference list of 13,406 emerging entities.…”

Section: Results and Analysismentioning

confidence: 99%

“…Similarly, if the target NE appears with unseen surface (mention), it is wrongly classified as an out-of-KB entity (false positives). [Hoffart et al, 2014], [Wu et al, 2016], and [Färber et al, 2016] proposed methods of classifying whether a given NE in a news article is out-of-KB. Their task is part of the task solved by [Nakashole et al, 2013] since the target NEs are given (assumed to be recognized).…”

Section: Out-of-kb Entity Identification On News Articlesmentioning

confidence: 99%

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Early Discovery of Emerging Entities in Microblogs

Akasaki

Yoshinaga

Toyoda

2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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Keeping up to date on emerging entities that appear every day is indispensable for various applications, such as social-trend analysis and marketing research. Previous studies have attempted to detect unseen entities that are not registered in a particular knowledge base as emerging entities and consequently find non-emerging entities since the absence of entities in knowledge bases does not guarantee their emergence. We therefore introduce a novel task of discovering truly emerging entities when they have just been introduced to the public through microblogs and propose an effective method based on time-sensitive distant supervision, which exploits distinctive early-stage contexts of emerging entities. Experimental results with a large-scale Twitter archive show that the proposed method achieves 83.2% precision of the top 500 discovered emerging entities, which outperforms baselines based on unseen entity recognition with burst detection. Besides notable emerging entities, our method can discover massive longtail and homographic emerging entities. An evaluation of relative recall shows that the method detects 80.4% emerging entities newly registered in Wikipedia; 92.4% of them are discovered earlier than their registration in Wikipedia, and the average lead-time is more than one year (571 days).

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Section: Results and Analysismentioning

confidence: 99%

Section: Out-of-kb Entity Identification On News Articlesmentioning

confidence: 99%

Early Discovery of Emerging Entities in Microblogs

Akasaki

Yoshinaga

Toyoda

2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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“…Emerging Entity Detection Streaming CDC is also related to the task of emerging entity detection (EED, Färber et al (2016)), which, given a mention that cannot be linked, seeks to predict whether it should produce a new KB entry. Although both tasks share similar motivations, they adopt different approaches (EED is formulated as a binary classification task), and CDC does not require deciding which entities should and should not be added to a knowledge base.…”

Section: Background and Motivationmentioning

confidence: 99%

Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference

Logan¹,

McCallum²,

Singh³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Streaming cross document entity coreference (CDC) systems disambiguate mentions of named entities in a scalable manner via incremental clustering. Unlike other approaches for named entity disambiguation (e.g., entity linking), streaming CDC allows for the disambiguation of entities that are unknown at inference time. Thus, it is well-suited for processing streams of data where new entities are frequently introduced. Despite these benefits, this task is currently difficult to study, as existing approaches are either evaluated on datasets that are no longer available, or omit other crucial details needed to ensure fair comparison. In this work, we address this issue by compiling a large benchmark adapted from existing free datasets, and performing a comprehensive evaluation of a number of novel and existing baseline models. 1 We investigate: how to best encode mentions, which clustering algorithms are most effective for grouping mentions, how models transfer to different domains, and how bounding the number of mentions tracked during inference impacts performance. Our results show that the relative performance of neural and feature-based mention encoders varies across different domains, and in most cases the best performance is achieved using a combination of both approaches. We also find that performance is minimally impacted by limiting the number of tracked mentions.

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“…1 According to [9], Wikidata describes the largest number of entities and comprises -in terms of entitiesother open knowledge graphs to a large extent. Consequently, this problem applies to all public knowledge graphs, and particularly so for long-tail and emerging entities [6].…”

Section: Introduction 1motivation and Problemmentioning

confidence: 99%

Information Extraction From Co-Occurring Similar Entities

Heist

Paulheim

2021

Proceedings of the Web Conference 2021

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Knowledge about entities and their interrelations is a crucial factor of success for tasks like question answering or text summarization. Publicly available knowledge graphs like Wikidata or DBpedia are, however, far from being complete. In this paper, we explore how information extracted from similar entities that co-occur in structures like tables or lists can help to increase the coverage of such knowledge graphs. In contrast to existing approaches, we do not focus on relationships within a listing (e.g., between two entities in a table row) but on the relationship between a listing's subject entities and the context of the listing. To that end, we propose a descriptive rule mining approach that uses distant supervision to derive rules for these relationships based on a listing's context. Extracted from a suitable data corpus, the rules can be used to extend a knowledge graph with novel entities and assertions. In our experiments we demonstrate that the approach is able to extract up to 3M novel entities and 30M additional assertions from listings in Wikipedia. We find that the extracted information is of high quality and thus suitable to extend Wikipedia-based knowledge graphs like DBpedia, YAGO, and CaLiGraph. For the case of DBpedia, this would result in an increase of covered entities by roughly 50%.

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On Emerging Entity Detection

Cited by 14 publications

References 8 publications

Early Discovery of Emerging Entities in Microblogs

Early Discovery of Emerging Entities in Microblogs

Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference

Information Extraction From Co-Occurring Similar Entities

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