17 skills found
facebookresearch / HypernymysuiteHearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora
johnbumgarner / WordhoardThis Python module can be used to obtain antonyms, synonyms, hypernyms, hyponyms, homophones and definitions.
vered1986 / UnsupervisedHypernymyData and code for the experiments in: "Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection". Vered Shwartz, Enrico Santus and Dominik Schlechtweg. EACL 2017.
gbcolborne / Hypernym DiscoveryA hypernym discovery system which learns to predict is-a relationships between words using projection learning
esantus / EVALutionDataset containing Semantic Relations and Metadata, for Training and Evaluating Distributional Semantic Models in English and Mandarin Chinese
KIZI / LinkedHypernymsDatasetNo description available
abyssnlp / Hearst Hypernym ExtractorHearst Patterns to extract Hypernyms from text
HillZhang1999 / Chinese Hypernym Hyponym Relation ExtractionCode & Data for our Paper "PATTERN-BASED CHINESE HYPERNYM-HYPONYM RELATION EXTRACTION METHOD"
Res-Tan / Hypernym Extractionhypernym extraction from definitions by recurrent neural networks using the part of speech information
uhh-lt / MangosteenA system for inducing distributional sense-aware semantic classes labeled with hypernyms
jacopofar / ItalianModelExtractorExtract a model of the Italian language for verb conjugations, PoS and hyponyms/hypernyms, using en.wiktionary, ConceptNet and WordNet
richardbaihe / RobustLMCode for paper "Better Language Model with Hypernym Class Prediction"
eraldoluis / Hyper BertUnsupervised hypernym discovery using BERT
TalMizrahii / Hearst PatternsThe project is divided into two parts, each of which focuses on detecting and extracting hypernyms and hyponyms.
abyssnlp / Hypernym LIBreA free Web-based Corpus for Hypernym Detection
DanSaada / Hearst PatternsA two-part project that makes use of regexs for the detection and extraction of hypernyms and hyponyms from a file in order to process them and create a new file with the relevant information.
esantus / ROOT9Datasets used in "Nine Features in a Random Forest to Learn Taxonomical Semantic Relations". Enrico Santus, Alessandro Lenci, Tin-Shing Chiu, Qin Lu and Chu-Ren Huang. LREC 2016
