Lexical Entailment
17 papers with code • 2 benchmarks • 5 datasets
Lexical Entailment is concerned with identifying the semantic relation, if any, holding between two words, as in (pigeon, hyponym, animal).
Source: Integrating Multiplicative Features into Supervised Distributional Methods for Lexical Entailment
Latest papers
TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Sematic Tasks
It achieves 11 SotA results, 4 top-2 results out of 16 tasks for the Taxonomy Enrichment, Hypernym Discovery, Taxonomy Construction, and Lexical Entailment tasks.
Continuous Entailment Patterns for Lexical Inference in Context
If we allow for tokens outside the PLM's vocabulary, patterns can be adapted more flexibly to a PLM's idiosyncrasies.
RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
In this paper, we introduce an advanced Russian general language understanding evaluation benchmark -- RussianGLUE.
Mining Knowledge for Natural Language Inference from Wikipedia Categories
Accurate lexical entailment (LE) and natural language inference (NLI) often require large quantities of costly annotations.
Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation
We address whether neural models for Natural Language Inference (NLI) can learn the compositional interactions between lexical entailment and negation, using four methods: the behavioral evaluation methods of (1) challenge test sets and (2) systematic generalization tasks, and the structural evaluation methods of (3) probes and (4) interventions.
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP
TextAttack also includes data augmentation and adversarial training modules for using components of adversarial attacks to improve model accuracy and robustness.
Discriminative Topic Mining via Category-Name Guided Text Embedding
We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora.
Generalized Tuning of Distributional Word Vectors for Monolingual and Cross-Lingual Lexical Entailment
Lexical entailment (LE; also known as hyponymy-hypernymy or is-a relation) is a core asymmetric lexical relation that supports tasks like taxonomy induction and text generation.
SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference
We present SherLIiC, a testbed for lexical inference in context (LIiC), consisting of 3985 manually annotated inference rule candidates (InfCands), accompanied by (i) ~960k unlabeled InfCands, and (ii) ~190k typed textual relations between Freebase entities extracted from the large entity-linked corpus ClueWeb09.
Specialising Word Vectors for Lexical Entailment
We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the IS-A or hyponymy-hypernymy relation.