Topic: lexicons Goto Github
Some thing interesting about lexicons
Some thing interesting about lexicons
lexicons,Auxiliary part for the diploma, on the topic Language features of verbalization of emotions in the crisis media discourse of 2020-2021
User: aigul2
lexicons,Using Polarity Lexicons and BERT Model for Supervised Learning With Russian Initially Unlabeled Datasets
User: antongolubev5
lexicons,Useful resources for hate speech detection in Spanish
User: fmplaza
lexicons,This project proposes a novel methodology to automatically learn financial lexicons that outperform the benchmark Loughran-McDonald lexicon in sentiment analysis tasks
User: hristijanpeshov
lexicons,The IEML language database. A git database containing the translations for IEML expressions: USL (Uniform Semantic Locator)
Organization: iemldev
Home Page: https://iemldev.github.io/ieml-language
lexicons,Language has a profound impact on our thoughts, perceptions, and conceptions of gender roles. Gender-inclusive language is, therefore, a key tool to promote social inclusion and contribute to achieving gender equality. Consequently, detecting and mitigating gender bias in texts is instrumental in halting its propagation and societal implications. However, there is a lack of gender bias datasets and lexicons for automating the detection of gender bias using supervised and unsupervised machine learning (ML) and natural language processing (NLP) techniques. Therefore, the main contribution of this work is to publicly provide labeled datasets and exhaustive lexicons by collecting, annotating, and augmenting relevant sentences to facilitate the detection of gender bias in English text. Towards this end, we present an updated version of our previously proposed taxonomy by re-formalizing its structure, adding a new bias type, and mapping each bias subtype to an appropriate detection methodology. The released datasets and lexicons span multiple bias subtypes including: Generic He, Generic She, Explicit Marking of Sex, and Gendered Neologisms. We leveraged the use of word embedding models to further augment the collected lexicons. The underlying motivation of our work is to enable the technical community to combat gender bias in text and halt its propagation using ML and NLP techniques.
User: jaddoughman
lexicons,Sentiment classifier and polarity lexicon creator
User: nameisxi
lexicons,Pre-trained models and language resources for Natural Language Processing in Polish
User: sdadas
lexicons,A model-driven transformation that generates SKOS thesauri from Ontolex lexica
Organization: unibz-core
Home Page: https://w3id.org/thor/ontolex2skos
lexicons,Domain Specific Lexicon Generation Project for DNLP Course @ PoliTo yy 2021/22, prof. Cagliero
User: wrongtactic
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