Papers by Richard Eckart de Castilho

4 papers
The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive Annotation (C18-2)

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Challenge: INCEpTION is an annotation platform for interactive and semantic annotation . the platform is both generic and modular .
Approach: INCEpTION is an annotation platform for interactive and semantic annotation . the platform incorporates machine learning capabilities which actively assist annotators .
Outcome: INCEpTION is an open-source annotation platform for tasks including interactive and semantic annotation.
From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains (2020.acl-main)

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Challenge: Existing approaches to disambiguate entity mentions in a text depend on training data.
Approach: They propose a domain-agnostic approach to annotate entities using a KB-based approach.
Outcome: The proposed approach outperforms existing methods in a simulation on difficult texts.
Integrating Knowledge-Supported Search into the INCEpTION Annotation Platform (D18-2)

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Challenge: Annotating entity mentions and linking them to a knowledge resource are essential tasks in many domains.
Approach: a new tool integrates knowledge-supported search and entity linking into INCEpTION . the tool allows users to search the corpus and create cross-document coreferences .
Outcome: a new tool integrates knowledge-supported search and entity linking into INCEpTION . the tool disambiguates mentions, introduces cross-document coreferences, and provides fast queries.
A Legal Perspective on Training Models for Natural Language Processing (L18-1)

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Challenge: a significant concern in processing natural language data is the unclear legal status of the input and output data/resources.
Approach: They examine which legal rules apply at relevant steps and how they affect the legal status of the results.
Outcome: The proposed model training process is based on three scenarios . the analysis focuses on which legal rules apply and how they affect the legal status of the results .

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