Hypernym Discovery
7 papers with code • 3 benchmarks • 1 datasets
Given a corpus and a target term (hyponym), the task of hypernym discovery consists of extracting a set of its most appropriate hypernyms from the corpus. For example, for the input word “dog”, some valid hypernyms would be “canine”, “mammal” or “animal”.
Benchmarks
These leaderboards are used to track progress in Hypernym Discovery
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SemEval-2018 Task 9: Hypernym Discovery
Most implemented papers
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Learning graph representations via low-dimensional embeddings that preserve relevant network properties is an important class of problems in machine learning.
Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection
The fundamental role of hypernymy in NLP has motivated the development of many methods for the automatic identification of this relation, most of which rely on word distribution.
CRIM at SemEval-2018 Task 9: A Hybrid Approach to Hypernym Discovery
gbcolborne/hypernym_discovery
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SEMEVAL 2018
This report describes the system developed by the CRIM team for the hypernym discovery task at SemEval 2018.
Meemi: A Simple Method for Post-processing and Integrating Cross-lingual Word Embeddings
yeraidm/meemi
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While monolingual word embeddings encode information about words in the context of a particular language, cross-lingual embeddings define a multilingual space where word embeddings from two or more languages are integrated together.
CogALex-VI Shared Task: Transrelation - A Robust Multilingual Language Model for Multilingual Relation Identification
We describe our submission to the CogALex-VI shared task on the identification of multilingual paradigmatic relations building on XLM-RoBERTa (XLM-R), a robustly optimized and multilingual BERT model.
CogALex 2.0: Impact of Data Quality on Lexical-Semantic Relation Prediction
Predicting lexical-semantic relations between word pairs has successfully been accomplished by pre-trained neural language models.
Modelling Commonsense Properties using Pre-Trained Bi-Encoders
amitgajbhiye/biencoder_concept_property
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COLING 2022
Grasping the commonsense properties of everyday concepts is an important prerequisite to language understanding.
