Search Results for author: Christiane Fellbaum

Found 20 papers, 3 papers with code

Linking WordNet to 3D Shapes

no code implementations GWC 2018 Angel X Chang, Rishi Mago, Pranav Krishna, Manolis Savva, Christiane Fellbaum

We describe a project to link the Princeton WordNet to 3D representations of real objects and scenes.

English WordNet 2019 – An Open-Source WordNet for English

1 code implementation GWC 2019 John P. McCrae, Alexandre Rademaker, Francis Bond, Ewa Rudnicka, Christiane Fellbaum

We describe the release of a new wordnet for English based on the Princeton WordNet, but now developed under an open-source model.

Building ASLNet, a Wordnet for American Sign Language

no code implementations GWC 2019 Colin Lualdi, Jack Hudson, Christiane Fellbaum, Noah Buchholz

We discuss the creation of ASLNet by aligning the Princeton WordNet (PWN) with SignStudy, an online database of American Sign Language (ASL) signs.

Interdependencies of Gender and Race in Contextualized Word Embeddings

no code implementations GeBNLP (COLING) 2020 May Jiang, Christiane Fellbaum

Recent years have seen a surge in research on the biases in word embeddings with respect to gender and, to a lesser extent, race.

Word Embeddings

Tuning Hierarchies in Princeton WordNet

no code implementations GWC 2016 Ahti Lohk, Christiane Fellbaum, Leo Vohandu

Many new wordnets in the world are constantly created and most take the original Princeton WordNet (PWN) as their starting point.

An Analysis of WordNet’s Coverage of Gender Identity Using Twitter and The National Transgender Discrimination Survey

no code implementations GWC 2016 Amanda Hicks, Michael Rutherford, Christiane Fellbaum, Jiang Bian

While gender identities in the Western world are typically regarded as binary, our previous work (Hicks et al., 2015) shows that there is more lexical variety of gender identity and the way people identify their gender.

CILI: the Collaborative Interlingual Index

no code implementations GWC 2016 Francis Bond, Piek Vossen, John McCrae, Christiane Fellbaum

This paper introduces the motivation for and design of the Collaborative InterLingual Index (CILI).

MABEL: Attenuating Gender Bias using Textual Entailment Data

1 code implementation26 Oct 2022 Jacqueline He, Mengzhou Xia, Christiane Fellbaum, Danqi Chen

To this end, we propose MABEL (a Method for Attenuating Gender Bias using Entailment Labels), an intermediate pre-training approach for mitigating gender bias in contextualized representations.

Contrastive Learning Fairness +1

Mitigating Gender Bias in Machine Translation through Adversarial Learning

no code implementations20 Mar 2022 Eve Fleisig, Christiane Fellbaum

Machine translation and other NLP systems often contain significant biases regarding sensitive attributes, such as gender or race, that worsen system performance and perpetuate harmful stereotypes.

Machine Translation Translation

Extending and Improving Wordnet via Unsupervised Word Embeddings

no code implementations29 Apr 2017 Mikhail Khodak, Andrej Risteski, Christiane Fellbaum, Sanjeev Arora

Our methods require very few linguistic resources, thus being applicable for Wordnet construction in low-resources languages, and may further be applied to sense clustering and other Wordnet improvements.

Clustering Word Embeddings

Automated WordNet Construction Using Word Embeddings

1 code implementation WS 2017 Mikhail Khodak, Andrej Risteski, Christiane Fellbaum, Sanjeev Arora

To evaluate our method we construct two 600-word testsets for word-to-synset matching in French and Russian using native speakers and evaluate the performance of our method along with several other recent approaches.

Information Retrieval Machine Translation +3

Encoding Adjective Scales for Fine-grained Resources

no code implementations LREC 2016 C{\'e}dric Lopez, Fr{\'e}d{\'e}rique Segond, Christiane Fellbaum

We propose an automatic approach towards determining the relative location of adjectives on a common scale based on their strength.

The MASC Word Sense Corpus

no code implementations LREC 2012 Rebecca J. Passonneau, Collin F. Baker, Christiane Fellbaum, Nancy Ide

The MASC project has produced a multi-genre corpus with multiple layers of linguistic annotation, together with a sentence corpus containing WordNet 3. 1 sense tags for 1000 occurrences of each of 100 words produced by multiple annotators, accompanied by indepth inter-annotator agreement data.

Empirical Comparisons of MASC Word Sense Annotations

no code implementations LREC 2012 Gerard de Melo, Collin F. Baker, Nancy Ide, Rebecca J. Passonneau, Christiane Fellbaum

We analyze how different conceptions of lexical semantics affect sense annotations and how multiple sense inventories can be compared empirically, based on annotated text.

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