Discovering emerging entities with ambiguous names

Hoffart, Johannes; Altün, Yasemin; Weikum, Gerhard

doi:10.1145/2566486.2568003

Cited by 100 publications

(87 citation statements)

References 16 publications

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“…Emerging entities have received some attention entity linking approaches (Hoffart et al, 2014;far, 2016;NIST, 2017). In particular for entity linking, identifying whether an entity is present in a knowledge base to prevent an erroneous link from being created is a key problem.…”

Section: Related Workmentioning

confidence: 99%

Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition

Derczynski¹,

Nichols²,

Erp³

et al. 2017

Proceedings of the 3rd Workshop on Noisy User-Generated Text

280

194

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This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recall on them is a real problem in noisy text -even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet "so.. kktny in 30 mins?!" -even human experts find the entity kktny hard to detect and resolve. The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities. The task as described in this paper evaluated the ability of participating entries to detect and classify novel and emerging named entities in noisy text.

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Section: Related Workmentioning

confidence: 99%

Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition

Derczynski¹,

Nichols²,

Erp³

et al. 2017

Proceedings of the 3rd Workshop on Noisy User-Generated Text

280

194

View full text Add to dashboard Cite

show abstract

“…The first is to identify which part of words (denoted as a mention) in a document refers to an entity [5]- [9]. The second problem is to identify which entity is referred to by that mention [1], [2], [10]- [12]. Some studies have attempted to address both problems together to identify the referred entity by the detected mention [7].…”

Section: Related Workmentioning

confidence: 99%

“…For identifying entities, one proposed mining additional context in addition to the knowledge base [13] and one proposed time-and-space-efficient algorithms [14]. Some tackled the entity identification problem on well-written documents [10], [13] and on short-and-noisy microblogs such as tweets [5]- [9]. To generate referred entities for a given mention, most studies used the name of entities (denoted as direct reference in our study).…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate a referred entity, the features used in previous studies include the probability that mentions entity's name [7], [15], the similarity between the context of entities and a document [10], [13], [16], the correlation between multiple entities [17]- [19], and user interest, which was estimated from user's microblogs [12]. As we discussed in our experiments, these features were not effective for indirect references.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Entity Identification on Microblogs by CRF Model with Adaptive Dependency

Katô

Yamamoto

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYWe address the problem of entity identification on a microblog with special attention to indirect reference cases in which entities are not referred to by their names. Most studies on identifying entities referred to them by their full/partial name or abbreviation, while there are many indirectly mentioned entities in microblogs, which are difficult to identify in short text such as microblogs. We therefore tackled indirect reference cases by developing features that are particularly important for certain types of indirect references and modeling dependency among referred entities by a Conditional Random Field (CRF) model. In addition, we model non-sequential order dependency while keeping the inference tractable by dynamically building dependency among entities. The experimental results suggest that our features were effective for indirect references, and our CRF model with adaptive dependency was robust even when there were multiple mentions in a microblog and achieved the same high performance as that with the fully connected CRF model.

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“…This makes it possible for us to leverage a Wikipedia-based resource such as DBpedia in a straightforward way, and evaluate the contribution of a wide-coverage knowledge base for our problem. An 'open' definition of entities, like the one found in [13], instead, opens up new problems such as how to detect and include new entities into the background knowledge base: although recently there have been attempts to address this problem at web scale [20], we leave this issue for future work.…”

Section: Related Workmentioning

confidence: 99%

Ranking Entities for Web Queries Through Text and Knowledge

Schuhmacher

Dietz

Ponzetto

2015

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

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When humans explain complex topics, they naturally talk about involved entities, such as people, locations, or events. In this paper, we aim at automating this process by retrieving and ranking entities that are relevant to understand free-text web-style queries like Argentine British relations, which typically demand a set of heterogeneous entities with no specific target type like, for instance, Falklands_-War or Margaret_Thatcher, as answer. Standard approaches to entity retrieval rely purely on features from the knowledge base. We approach the problem from the opposite direction, namely by analyzing web documents that are found to be query-relevant. Our approach hinges on entity linking technology that identifies entity mentions and links them to a knowledge base like Wikipedia. We use a learning-to-rank approach and study different features that use documents, entity mentions, and knowledge base entities -thus bridging document and entity retrieval. Since established benchmarks for this problem do not exist, we use TREC test collections for document ranking and collect custom relevance judgments for entities. Experiments on TREC Robust04 and TREC Web13/14 data show that: i) single entity features, like the frequency of occurrence within the top-ranked documents, or the query retrieval score against a knowledge base, perform generally well; ii) the best overall performance is achieved when combining different features that relate an entity to the query, its document mentions, and its knowledge base representation.

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Discovering emerging entities with ambiguous names

Cited by 100 publications

References 16 publications

Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition

Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition

Entity Identification on Microblogs by CRF Model with Adaptive Dependency

Ranking Entities for Web Queries Through Text and Knowledge

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