ZenCrowd

Demartini, Gianluca; Difallah, Djellel Eddine; Cudré-Mauroux, Philippe

doi:10.1145/2187836.2187900

Cited by 360 publications

(38 citation statements)

References 29 publications

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“…One way to address this data challenge is through crowdsourcing such corpora in their entirety [45]. A cheaper approach could be to integrate crowdsourcing within the NER and NEL algorithms themselves [59]. In this way, NE mentions that can be linked automatically and with high confidence to instances in the Linked Open Data cloud, will not need to be shown to the human annotators.…”

Section: Resultsmentioning

confidence: 99%

Analysis of named entity recognition and linking for tweets

Derczynski

Maynard

Rizzo

et al. 2015

Information Processing & Management

285

186

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Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area. Unlike carefully authored news text and other longer content, tweets pose a number of new challenges, due to their short, noisy, context-dependent, and dynamic nature. Information extraction from tweets is typically performed in a pipeline, comprising consecutive stages of language identification, tokenisation, part-of-speech tagging, named entity recognition and entity disambiguation (e.g. with respect to DBpedia). In this work, we describe a new Twitter entity disambiguation dataset, and conduct an empirical analysis of named entity recognition and disambiguation, investigating how robust a number of state-of-the-art systems are on such noisy texts, what the main sources of error are, and which problems should be further investigated to improve the state of the art.

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Section: Resultsmentioning

confidence: 99%

Analysis of named entity recognition and linking for tweets

Derczynski

Maynard

Rizzo

et al. 2015

Information Processing & Management

285

186

View full text Add to dashboard Cite

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“…Multi-user scenarios include CrowdMap [19] for ontology matching, ZenCrowd [8] for entity linking, and Zhang et al [23] for database schema matching, which use crowdsourcing on a web platform.…”

Section: Multi-user Feedbackmentioning

confidence: 99%

Pay-As-You-Go Multi-user Feedback Model for Ontology Matching

Cruz

Loprete

Palmonari

et al. 2014

Lecture Notes in Computer Science

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Abstract.Using a pay-as-you-go strategy, we allow for a community of users to validate or invalidate mappings obtained by an automatic ontology matching system using consensus for each mapping. The ultimate objectives are effectiveness-improving the quality of the obtained alignment (set of mappings) measured in terms of F-measure as a function of the number of user interactionsand robustness-making the system as much as possible impervious to user validation errors. Our strategy consists of two major steps: candidate mapping selection, which ranks mappings based on their perceived quality so that users are presented first with those mappings with lowest quality, and feedback propagation, which seeks to validate or invalidate those mappings that are perceived to be similar to the mappings already presented to the users for validation. The purpose of these two strategies is twofold: achieve greater improvements earlier and minimize overall user interaction. There are three important features of our approach: the use of a dynamic ranking mechanism to adapt to the new conditions after each user interaction, the presentation of each mapping for validation more than once-revalidation-because of possible user errors, and the immediate propagation of the user input on a mapping without first achieving consensus for that mapping. We study extensively the effectiveness and robustness of our approach as several of these parameters change, namely the error and revalidation rates, as a function of the number of iterations, to provide conclusive guidelines for the design and implementation of multi-user feedback ontology matching systems.

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“…By introducing crowdsourcing techniques into NED tasks, ZenCrowd (Demartini et al, 2012) dynamically generates micro-tasks on an online crowdsourcing platform and takes the advantage of human intelligence to improve the quality of the links. Moreover, a probabilistic framework is devised in ZenCrowd to identify unreliable human workers and make sensible decisions for entity linking, that is, inferring ground truth of entity linking from noisy labeling information by a crowd of non-experts.…”

Section: Related Workmentioning

confidence: 99%

Disambiguating named entities with deep supervised learning via crowd labels

Zhou

Tang

Xiao

et al. 2017

Frontiers Inf Technol Electronic Eng

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Named entity disambiguation (NED) is the task of linking mentions of ambiguous entities to their referenced entities in a knowledge base such as Wikipedia. We propose an approach to effectively disentangle the discriminative features in the manner of collaborative utilization of collective wisdom (via human-labeled crowd labels) and deep learning (via human-generated data) for the NED task. In particular, we devise a crowd model to elicit the underlying features (crowd features) from crowd labels that indicate a matching candidate for each mention, and then use the crowd features to fine-tune a dynamic convolutional neural network (DCNN). The learned DCNN is employed to obtain deep crowd features to enhance traditional hand-crafted features for the NED task. The proposed method substantially benefits from the utilization of crowd knowledge (via crowd labels) into a generic deep learning for the NED task. Experimental analysis demonstrates that the proposed approach is superior to the traditional hand-crafted features when enough crowd labels are gathered.

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ZenCrowd

Cited by 360 publications

References 29 publications

Analysis of named entity recognition and linking for tweets

Analysis of named entity recognition and linking for tweets

Pay-As-You-Go Multi-user Feedback Model for Ontology Matching

Disambiguating named entities with deep supervised learning via crowd labels

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