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.
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.
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.
no code implementations • EACL (GWC) 2021 • Colin Lualdi, Elaine Wright, Jack Hudson, Naomi Caselli, Christiane Fellbaum
We report on the development of ASLNet, a wordnet for American Sign Language (ASL).
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.
no code implementations • Findings (EMNLP) 2021 • Julie Kallini, Christiane Fellbaum
Coordination is a phenomenon of language that conjoins two or more terms or phrases using a coordinating conjunction.
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.
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.
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).
no code implementations • 11 Mar 2024 • Ioana Marinescu, Christiane Fellbaum
Determining the intended, context-dependent meanings of noun compounds like "shoe sale" and "fire sale" remains a challenge for NLP.
2 code implementations • 26 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.
no code implementations • 20 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.
no code implementations • 29 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.
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.
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.
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.
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.