Lexical Entailment
16 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
Benchmarks
These leaderboards are used to track progress in Lexical Entailment
| Trend | Dataset | Best Model | Paper | Code | Compare |
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HyperLex
EVALution
SherLIiC
Most implemented papers
Hierarchical Density Order Embeddings
benathi/density-order-emb
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ICLR 2018
By representing words with probability densities rather than point vectors, probabilistic word embeddings can capture rich and interpretable semantic information and uncertainty.
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP
QData/TextAttack
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EMNLP 2020
TextAttack also includes data augmentation and adversarial training modules for using components of adversarial attacks to improve model accuracy and robustness.
RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
RussianNLP/RussianSuperGLUE
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EMNLP 2020
In this paper, we introduce an advanced Russian general language understanding evaluation benchmark -- RussianGLUE.
Experiments with Three Approaches to Recognizing Lexical Entailment
Two general strategies for RLE have been proposed: One strategy is to manually construct an asymmetric similarity measure for context vectors (directional similarity) and another is to treat RLE as a problem of learning to recognize semantic relations using supervised machine learning techniques (relation classification).
Representing Meaning with a Combination of Logical and Distributional Models
In this paper, we focus on the three components of a practical system integrating logical and distributional models: 1) Parsing and task representation is the logic-based part where input problems are represented in probabilistic logic.
A Consolidated Open Knowledge Representation for Multiple Texts
We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner.
Specialising Word Vectors for Lexical Entailment
nmrksic/lear
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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.
Scoring Lexical Entailment with a Supervised Directional Similarity Network
Experiments show excellent performance on scoring graded lexical entailment, raising the state-of-the-art on the HyperLex dataset by approximately 25%.
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.
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.
