Richard Eckart de Castilho

Also published as: Richard Eckart de Castilho


2021

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Human-In-The-LoopEntity Linking for Low Resource Domains
Jan-Christoph Klie | Richard Eckart de Castilho | Iryna Gurevych
Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances

Entity linking (EL) is concerned with disambiguating entity mentions in a text against knowledge bases (KB). To quickly annotate texts with EL even in low-resource domains and noisy text, we present a novel Human-In-The-Loop EL approach. We show that it greatly outperforms a strong baseline in simulation. In a user study, annotation time is reduced by 35 % compared to annotating without interactive support; users report that they strongly prefer our system over ones without. An open-source and ready-to-use implementation based on the text annotation platform is made available.

2020

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From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains
Jan-Christoph Klie | Richard Eckart de Castilho | Iryna Gurevych
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Entity linking (EL) is concerned with disambiguating entity mentions in a text against knowledge bases (KB). It is crucial in a considerable number of fields like humanities, technical writing and biomedical sciences to enrich texts with semantics and discover more knowledge. The use of EL in such domains requires handling noisy texts, low resource settings and domain-specific KBs. Existing approaches are mostly inappropriate for this, as they depend on training data. However, in the above scenario, there exists hardly annotated data, and it needs to be created from scratch. We therefore present a novel domain-agnostic Human-In-The-Loop annotation approach: we use recommenders that suggest potential concepts and adaptive candidate ranking, thereby speeding up the overall annotation process and making it less tedious for users. We evaluate our ranking approach in a simulation on difficult texts and show that it greatly outperforms a strong baseline in ranking accuracy. In a user study, the annotation speed improves by 35% compared to annotating without interactive support; users report that they strongly prefer our system. An open-source and ready-to-use implementation based on the text annotation platform INCEpTION (https://inception-project.github.io) is made available.

2019

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A Multi-Platform Annotation Ecosystem for Domain Adaptation
Richard Eckart de Castilho | Nancy Ide | Jin-Dong Kim | Jan-Christoph Klie | Keith Suderman
Proceedings of the 13th Linguistic Annotation Workshop

This paper describes an ecosystem consisting of three independent text annotation platforms. To demonstrate their ability to work in concert, we illustrate how to use them to address an interactive domain adaptation task in biomedical entity recognition. The platforms and the approach are in general domain-independent and can be readily applied to other areas of science.

2018

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Integrating Knowledge-Supported Search into the INCEpTION Annotation Platform
Beto Boullosa | Richard Eckart de Castilho | Naveen Kumar | Jan-Christoph Klie | Iryna Gurevych
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Annotating entity mentions and linking them to a knowledge resource are essential tasks in many domains. It disambiguates mentions, introduces cross-document coreferences, and the resources contribute extra information, e.g. taxonomic relations. Such tasks benefit from text annotation tools that integrate a search which covers the text, the annotations, as well as the knowledge resource. However, to the best of our knowledge, no current tools integrate knowledge-supported search as well as entity linking support. We address this gap by introducing knowledge-supported search functionality into the INCEpTION text annotation platform. In our approach, cross-document references are created by linking entity mentions to a knowledge base in the form of a structured hierarchical vocabulary. The resulting annotations are then indexed to enable fast and yet complex queries taking into account the text, the annotations, and the vocabulary structure.

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A Legal Perspective on Training Models for Natural Language Processing
Richard Eckart de Castilho | Giulia Dore | Thomas Margoni | Penny Labropoulou | Iryna Gurevych
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive Annotation
Jan-Christoph Klie | Michael Bugert | Beto Boullosa | Richard Eckart de Castilho | Iryna Gurevych
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

We introduce INCEpTION, a new annotation platform for tasks including interactive and semantic annotation (e.g., concept linking, fact linking, knowledge base population, semantic frame annotation). These tasks are very time consuming and demanding for annotators, especially when knowledge bases are used. We address these issues by developing an annotation platform that incorporates machine learning capabilities which actively assist and guide annotators. The platform is both generic and modular. It targets a range of research domains in need of semantic annotation, such as digital humanities, bioinformatics, or linguistics. INCEpTION is publicly available as open-source software.

2017

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Representation and Interchange of Linguistic Annotation. An In-Depth, Side-by-Side Comparison of Three Designs
Richard Eckart de Castilho | Nancy Ide | Emanuele Lapponi | Stephan Oepen | Keith Suderman | Erik Velldal | Marc Verhagen
Proceedings of the 11th Linguistic Annotation Workshop

For decades, most self-respecting linguistic engineering initiatives have designed and implemented custom representations for various layers of, for example, morphological, syntactic, and semantic analysis. Despite occasional efforts at harmonization or even standardization, our field today is blessed with a multitude of ways of encoding and exchanging linguistic annotations of these types, both at the levels of ‘abstract syntax’, naming choices, and of course file formats. To a large degree, it is possible to work within and across design plurality by conversion, and often there may be good reasons for divergent design reflecting differences in use. However, it is likely that some abstract commonalities across choices of representation are obscured by more superficial differences, and conversely there is no obvious procedure to tease apart what actually constitute contentful vs. mere technical divergences. In this study, we seek to conceptually align three representations for common types of morpho-syntactic analysis, pinpoint what in our view constitute contentful differences, and reflect on the underlying principles and specific requirements that led to individual choices. We expect that a more in-depth understanding of these choices across designs may led to increased harmonization, or at least to more informed design of future representations.

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A tool for extracting sense-disambiguated example sentences through user feedback
Beto Boullosa | Richard Eckart de Castilho | Alexander Geyken | Lothar Lemnitzer | Iryna Gurevych
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

This paper describes an application system aimed to help lexicographers in the extraction of example sentences for a given headword based on its different senses. The tool uses classification and clustering methods and incorporates user feedback to refine its results.

2016

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A Web-based Tool for the Integrated Annotation of Semantic and Syntactic Structures
Richard Eckart de Castilho | Éva Mújdricza-Maydt | Seid Muhie Yimam | Silvana Hartmann | Iryna Gurevych | Anette Frank | Chris Biemann
Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH)

We introduce the third major release of WebAnno, a generic web-based annotation tool for distributed teams. New features in this release focus on semantic annotation tasks (e.g. semantic role labelling or event annotation) and allow the tight integration of semantic annotations with syntactic annotations. In particular, we introduce the concept of slot features, a novel constraint mechanism that allows modelling the interaction between semantic and syntactic annotations, as well as a new annotation user interface. The new features were developed and used in an annotation project for semantic roles on German texts. The paper briefly introduces this project and reports on experiences performing annotations with the new tool. On a comparative evaluation, our tool reaches significant speedups over WebAnno 2 for a semantic annotation task.

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Automatic Analysis of Flaws in Pre-Trained NLP Models
Richard Eckart de Castilho
Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies (WLSI/OIAF4HLT2016)

Most tools for natural language processing today are based on machine learning and come with pre-trained models. In addition, third-parties provide pre-trained models for popular NLP tools. The predictive power and accuracy of these tools depends on the quality of these models. Downstream researchers often base their results on pre-trained models instead of training their own. Consequently, pre-trained models are an essential resource to our community. However, to be best of our knowledge, no systematic study of pre-trained models has been conducted so far. This paper reports on the analysis of 274 pre-models for six NLP tools and four potential causes of problems: encoding, tokenization, normalization and change over time. The analysis is implemented in the open source tool Model Investigator. Our work 1) allows model consumers to better assess whether a model is suitable for their task, 2) enables tool and model creators to sanity-check their models before distributing them, and 3) enables improvements in tool interoperability by performing automatic adjustments of normalization or other pre-processing based on the models used.

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Sense-annotating a Lexical Substitution Data Set with Ubyline
Tristan Miller | Mohamed Khemakhem | Richard Eckart de Castilho | Iryna Gurevych
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We describe the construction of GLASS, a newly sense-annotated version of the German lexical substitution data set used at the GermEval 2015: LexSub shared task. Using the two annotation layers, we conduct the first known empirical study of the relationship between manually applied word senses and lexical substitutions. We find that synonymy and hypernymy/hyponymy are the only semantic relations directly linking targets to their substitutes, and that substitutes in the target’s hypernymy/hyponymy taxonomy closely align with the synonyms of a single GermaNet synset. Despite this, these substitutes account for a minority of those provided by the annotators. The results of our analysis accord with those of a previous study on English-language data (albeit with automatically induced word senses), leading us to suspect that the sense―substitution relations we discovered may be of a universal nature. We also tentatively conclude that relatively cheap lexical substitution annotations can be used as a knowledge source for automatic WSD. Also introduced in this paper is Ubyline, the web application used to produce the sense annotations. Ubyline presents an intuitive user interface optimized for annotating lexical sample data, and is readily adaptable to sense inventories other than GermaNet.

2015

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In-tool Learning for Selective Manual Annotation in Large Corpora
Erik-Lân Do Dinh | Richard Eckart de Castilho | Iryna Gurevych
Proceedings of ACL-IJCNLP 2015 System Demonstrations

2014

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A broad-coverage collection of portable NLP components for building shareable analysis pipelines
Richard Eckart de Castilho | Iryna Gurevych
Proceedings of the Workshop on Open Infrastructures and Analysis Frameworks for HLT

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Automatic Annotation Suggestions and Custom Annotation Layers in WebAnno
Seid Muhie Yimam | Chris Biemann | Richard Eckart de Castilho | Iryna Gurevych
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations

2013

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WebAnno: A Flexible, Web-based and Visually Supported System for Distributed Annotations
Seid Muhie Yimam | Iryna Gurevych | Richard Eckart de Castilho | Chris Biemann
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

2012

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CSNIPER - Annotation-by-query for Non-canonical Constructions in Large Corpora
Richard Eckart de Castilho | Sabine Bartsch | Iryna Gurevych
Proceedings of the ACL 2012 System Demonstrations