Search Results for author: Rob van der Goot

Found 43 papers, 21 papers with code

Biomedical Event Extraction as Sequence Labeling

no code implementations EMNLP 2020 Alan Ramponi, Rob van der Goot, Rosario Lombardo, Barbara Plank

We introduce Biomedical Event Extraction as Sequence Labeling (BeeSL), a joint end-to-end neural information extraction model.

Event Extraction Multi-Task Learning

CL-MoNoise: Cross-lingual Lexical Normalization

no code implementations EMNLP (WNUT) 2021 Rob van der Goot

In this paper, we are the first to propose a model for cross-lingual normalization, with which we participate in the WNUT 2021 shared task.

Lexical Normalization

Much Gracias: Semi-supervised Code-switch Detection for Spanish-English: How far can we get?

no code implementations NAACL (CALCS) 2021 Dana-Maria Iliescu, Rasmus Grand, Sara Qirko, Rob van der Goot

Existing models for language identification in code-switched data are all supervised, requiring annotated training data which is only available for a limited number of language pairs.

Language Identification

We Need to Talk About train-dev-test Splits

1 code implementation EMNLP 2021 Rob van der Goot

However, the introduction of neural networks in NLP has led to a different use of these standard splits; the development set is now often used for model selection during the training procedure.

Model Selection

NLP North at WNUT-2020 Task 2: Pre-training versus Ensembling for Detection of Informative COVID-19 English Tweets

no code implementations EMNLP (WNUT) 2020 Anders Giovanni Møller, Rob van der Goot, Barbara Plank

With the COVID-19 pandemic raging world-wide since the beginning of the 2020 decade, the need for monitoring systems to track relevant information on social media is vitally important.

Challenges in Annotating and Parsing Spoken, Code-switched, Frisian-Dutch Data

1 code implementation EACL (AdaptNLP) 2021 Anouck Braggaar, Rob van der Goot

The best single source treebank (nl_alpino) resulted in an LAS of 54. 7 whereas our data selection outperformed the single best transfer treebank and led to 55. 6 LAS on the test data.

XLM-R

Spectral Probing

1 code implementation21 Oct 2022 Max Müller-Eberstein, Rob van der Goot, Barbara Plank

Linguistic information is encoded at varying timescales (subwords, phrases, etc.)

Informativeness

Skill Extraction from Job Postings using Weak Supervision

1 code implementation16 Sep 2022 Mike Zhang, Kristian Nørgaard Jensen, Rob van der Goot, Barbara Plank

Aggregated data obtained from job postings provide powerful insights into labor market demands, and emerging skills, and aid job matching.

Sort by Structure: Language Model Ranking as Dependency Probing

no code implementations NAACL 2022 Max Müller-Eberstein, Rob van der Goot, Barbara Plank

Making an informed choice of pre-trained language model (LM) is critical for performance, yet environmentally costly, and as such widely underexplored.

Language Modelling Structured Prediction

Experimental Standards for Deep Learning in Natural Language Processing Research

1 code implementation13 Apr 2022 Dennis Ulmer, Elisa Bassignana, Max Müller-Eberstein, Daniel Varab, Mike Zhang, Rob van der Goot, Christian Hardmeier, Barbara Plank

The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well.

Probing for Labeled Dependency Trees

1 code implementation ACL 2022 Max Müller-Eberstein, Rob van der Goot, Barbara Plank

Probing has become an important tool for analyzing representations in Natural Language Processing (NLP).

Dependency Parsing Informativeness

Genre as Weak Supervision for Cross-lingual Dependency Parsing

1 code implementation EMNLP 2021 Max Müller-Eberstein, Rob van der Goot, Barbara Plank

Recent work has shown that monolingual masked language models learn to represent data-driven notions of language variation which can be used for domain-targeted training data selection.

Dependency Parsing

Lexical Normalization for Code-switched Data and its Effect on POS Tagging

1 code implementation EACL 2021 Rob van der Goot, {\"O}zlem {\c{C}}etino{\u{g}}lu

Lexical normalization, the translation of non-canonical data to standard language, has shown to improve the performance of many natural language processing tasks on social media.

Lexical Normalization POS +1

On the Effectiveness of Dataset Embeddings in Mono-lingual,Multi-lingual and Zero-shot Conditions

no code implementations EACL (AdaptNLP) 2021 Rob van der Goot, Ahmet Üstün, Barbara Plank

However, it remains unclear in which situations these dataset embeddings are most effective, because they are used in a large variety of settings, languages and tasks.

Dependency Parsing Lemmatization +1

Creating a Universal Dependencies Treebank of Spoken Frisian-Dutch Code-switched Data

no code implementations22 Feb 2021 Anouck Braggaar, Rob van der Goot

This paper explores the difficulties of annotating transcribed spoken Dutch-Frisian code-switch utterances into Universal Dependencies.

Sentence segmentation

Lexical Normalization for Code-switched Data and its Effect on POS-tagging

no code implementations1 Jun 2020 Rob van der Goot, Özlem Çetinoğlu

Lexical normalization, the translation of non-canonical data to standard language, has shown to improve the performance of manynatural language processing tasks on social media.

Language Identification Lexical Normalization +2

Norm It! Lexical Normalization for Italian and Its Downstream Effects for Dependency Parsing

no code implementations LREC 2020 Rob van der Goot, Alan Ramponi, Tommaso Caselli, Michele Cafagna, Lorenzo De Mattei

However, for Italian, there is no benchmark available for lexical normalization, despite the presence of many benchmarks for other tasks involving social media data.

Dependency Parsing Lexical Normalization

MoNoise: A Multi-lingual and Easy-to-use Lexical Normalization Tool

1 code implementation ACL 2019 Rob van der Goot

In this paper, we introduce and demonstrate the online demo as well as the command line interface of a lexical normalization system (MoNoise) for a variety of languages.

Lexical Normalization

sthruggle at SemEval-2019 Task 5: An Ensemble Approach to Hate Speech Detection

no code implementations SEMEVAL 2019 Aria Nourbakhsh, Frida Vermeer, Gijs Wiltvank, Rob van der Goot

In this paper, we present our approach to detection of hate speech against women and immigrants in tweets for our participation in the SemEval-2019 Task 5.

Hate Speech Detection Word Embeddings

Fair is Better than Sensational:Man is to Doctor as Woman is to Doctor

1 code implementation23 May 2019 Malvina Nissim, Rik van Noord, Rob van der Goot

However, beside the intrinsic problems with the analogy task as a bias detection tool, in this paper we show that a series of issues related to how analogies have been implemented and used might have yielded a distorted picture of bias in word embeddings.

Bias Detection Word Embeddings

Modeling Input Uncertainty in Neural Network Dependency Parsing

1 code implementation EMNLP 2018 Rob van der Goot, Gertjan van Noord

Recently introduced neural network parsers allow for new approaches to circumvent data sparsity issues by modeling character level information and by exploiting raw data in a semi-supervised setting.

Dependency Parsing Lexical Normalization +1

Bleaching Text: Abstract Features for Cross-lingual Gender Prediction

1 code implementation ACL 2018 Rob van der Goot, Nikola Ljubešić, Ian Matroos, Malvina Nissim, Barbara Plank

Gender prediction has typically focused on lexical and social network features, yielding good performance, but making systems highly language-, topic-, and platform-dependent.

Gender Prediction

MoNoise: Modeling Noise Using a Modular Normalization System

2 code implementations10 Oct 2017 Rob van der Goot, Gertjan van Noord

We show that MoNoise beats the state-of-the-art on different normalization benchmarks for English and Dutch, which all define the task of normalization slightly different.

Lexical Normalization Spelling Correction +1

The Denoised Web Treebank: Evaluating Dependency Parsing under Noisy Input Conditions

no code implementations LREC 2016 Joachim Daiber, Rob van der Goot

We introduce the Denoised Web Treebank: a treebank including a normalization layer and a corresponding evaluation metric for dependency parsing of noisy text, such as Tweets.

Dependency Parsing Lexical Normalization +2

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