Medical WordNet: A new methodology for the construction and validation of information resources for consumer health

A consumer health information system must be able to comprehend both expert and non-expert medical vocabulary and to map between the two. We describe an ongoing project to create a new lexical database called Medical WordNet (MWN), consisting of medically relevant terms used by and intelligible to non-expert subjects and supplemented by a corpus of natural-language sentences that is designed to provide medically validated contexts for MWN terms. The corpus derives primarily from online health information sources targeted to consumers, and involves two sub-corpora, called Medical FactNet (MFN) and Medical BeliefNet (MBN), respectively. The former consists of statements accredited as true on the basis of a rigorous process of validation, the latter of statements which non-experts believe to be true. We summarize the MWN / MFN / MBN project, and describe some of its applications

WordNet: An Electronic Lexical Reference System Based on Theories of Lexical Memory

Cet article fait la description de WordNet, système de référence électronique, dont le dessin est basé sur des théories psycholinguistiques concernant la mémoire lexicale et l’organisation mentale des mots.Les noms, les verbes et les adjectifs anglais sont organisés en groupes synonymes (les « synsets »), chacun représentant un concept lexical. Trois relations principales — l’hyponymie, la méronymie et l’antonymie — servent à établir les rapports conceptuels entre les « synsets ». Les présuppositions qui lient les verbes sont indiquées ainsi que leurs contextes syntaxiques et sémantiques.En tâchant de miroiter l’organisation mentale des concepts lexicaux, WordNet pourrait servir l’utilisateur sans formation en linguistique.This paper describes WordNet, an on-line lexical reference system whose design is based on psycholinguistic theories of human lexical organization and memory.English nouns, verbs, and adjectives are organized into synonym sets, each representing one underlying lexical concept. Synonym sets are then related via three principal conceptual relations: hyponymy, meronymy, and antonymy. Verbs are additionally specified for presupposition relations that hold among them, and for their most common semantic/syntactic frames.By attempting to mirror the organization of the mental lexicon, WordNet strives to serve the linguistically unsophisticated user

MABEL: Attenuating Gender Bias using Textual Entailment Data

Pre-trained language models encode undesirable social biases, which are further exacerbated in downstream use. 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. Key to our approach is the use of a contrastive learning objective on counterfactually augmented, gender-balanced entailment pairs from natural language inference (NLI) datasets. We also introduce an alignment regularizer that pulls identical entailment pairs along opposite gender directions closer. We extensively evaluate our approach on intrinsic and extrinsic metrics, and show that MABEL outperforms previous task-agnostic debiasing approaches in terms of fairness. It also preserves task performance after fine-tuning on downstream tasks. Together, these findings demonstrate the suitability of NLI data as an effective means of bias mitigation, as opposed to only using unlabeled sentences in the literature. Finally, we identify that existing approaches often use evaluation settings that are insufficient or inconsistent. We make an effort to reproduce and compare previous methods, and call for unifying the evaluation settings across gender debiasing methods for better future comparison.Comment: Accepted to EMNLP 2022. Code and models are publicly available at https://github.com/princeton-nlp/mabe

Towards Foundational Semantics - Ontological Semantics Revisited -

Cimiano P, Reyle U. Towards Foundational Semantics - Ontological Semantics Revisited -. In: Bennett B, Fellbaum C, eds. Formal Ontology in Information Systems, Proceedings of the Fourth International Conference, FOIS 2006. Frontiers in Artificial Intelligence and Applications, 150. IOS Press; 2006: 51-62

Publishing and Linking WordNet using lemon and RDF

McCrae J, Fellbaum C, Cimiano P. Publishing and Linking WordNet using lemon and RDF. In: Proceedings of the 3rd Workshop on Linked Data in Linguistics. 2014

The Compositional Nature of Verb and Argument Representations in the Human Brain

How does the human brain represent simple compositions of objects, actors,and actions? We had subjects view action sequence videos during neuroimaging (fMRI) sessions and identified lexical descriptions of those videos by decoding (SVM) the brain representations based only on their fMRI activation patterns. As a precursor to this result, we had demonstrated that we could reliably and with high probability decode action labels corresponding to one of six action videos (dig, walk, etc.), again while subjects viewed the action sequence during scanning (fMRI). This result was replicated at two different brain imaging sites with common protocols but different subjects, showing common brain areas, including areas known for episodic memory (PHG, MTL, high level visual pathways, etc.,i.e. the 'what' and 'where' systems, and TPJ, i.e. 'theory of mind'). Given these results, we were also able to successfully show a key aspect of language compositionality based on simultaneous decoding of object class and actor identity. Finally, combining these novel steps in 'brain reading' allowed us to accurately estimate brain representations supporting compositional decoding of a complex event composed of an actor, a verb, a direction, and an object.Comment: 11 pages, 6 figure

SemEval-2010 Task 17: All-words Word Sense Disambiguation on a Specific Domain

Domain portability and adaptation of NLP components and Word Sense Disambiguation systems present new challenges. The difficulties found by supervised systems to adapt might change the way we assess the strengths and weaknesses of supervised and knowledge-based WSD systems. Unfortunately, all existing evaluation datasets for specific domains are lexical-sample corpora. This task presented all-words datasets on the environment domain for WSD in four languages (Chinese, Dutch, English, Italian). 11 teams participated, with supervised and knowledge-based systems, mainly in the English dataset. The results show that in all languages the participants where able to beat the most frequent sense heuristic as estimated from general corpora. The most successful approaches used some sort of supervision in the form of hand-tagged examples from the domain

KYOTO: A System for Mining, Structuring, and Distributing Knowledge Across Languages and Cultures

We outline work performed within the framework of a current EC project. The goal is to construct a language-independent information system for a specific domain (environment/ecology/biodiversity) anchored in a language-independent ontology that is linked to wordnets in seven languages. For each language, information extraction and identification of lexicalized concepts with ontological entries is carried out by text miners (?Kybots?). The mapping of language-specific lexemes to the ontology allows for crosslinguistic identification and translation of equivalent terms. The infrastructure developed within this project enables long-range knowledge sharing and transfer across many languages and cultures, addressing the need for global and uniform transition of knowledge beyond the specific domains addressed here