| Challenge: | Semantic graphs encode the structural qualities of language as a representation of human knowledge. |
| Approach: | They propose a global-theoretic model that integrates global and local properties of semantic graphs to improve local prediction of relational relations between synsets. |
| Outcome: | The proposed model improves on the local task of predicting semantic relations between synsets, yielding state-of-the-art results on the WN18RR dataset. |
Similar Papers
Introducing Graph Context into Language Models through Parameter-Efficient Fine-Tuning for Lexical Relation Mining (2025.acl-long)
Copied to clipboard
| Challenge: | Pre-trained language models can effectively mine lexical relations between word pairs . however, graph features and semantic knowledge of pre-tried models are lacking in the task. |
| Approach: | They propose a parameter-efficient fine-tuning method which integrates graph features and semantic representations for lexical relation classification and lexic entailment tasks. |
| Outcome: | The proposed method integrates graph features and semantic representations for lexical relation mining tasks. |
Rationalizing Medical Relation Prediction from Corpus-level Statistics (2020.acl-main)
Copied to clipboard
| Challenge: | Existing work on predicting relations based on text corpus has focused on analyzing raw texts mentioning two entities. |
| Approach: | They propose a framework that can be used to rationalize medical relation prediction . they recall contexts associated with the target entities and recognize relational interactions between them . |
| Outcome: | The proposed framework can achieve competitive predictive performance against a comprehensive list of neural baseline models, and present rationales to justify its prediction. |
Syn2Vec: Synset Colexification Graphs for Lexical Semantic Similarity (2022.naacl-main)
Copied to clipboard
| Challenge: | In this paper we examine patterns of colexification as an aspect of lexical-semantic organization, and compare several approaches to build large scale graphs across 499 world languages. |
| Approach: | They propose to use patterns of colexification as an aspect of lexical-semantic organization to build large scale synset graphs across a typologically diverse set of 499 world languages. |
| Outcome: | The proposed models are evaluated against human judgments on a semantic similarity task for nine languages. |
Network Features Based Co-hyponymy Detection (L18-1)
Copied to clipboard
| Challenge: | Existing methods to detect lexical relations have been used to identify them in both supervised and unsupervised ways. |
| Approach: | They propose to use distributional semantic models to detect co-hyponymy relation with high accuracy and various network measures to perform better or at par with the state-of-the-art models. |
| Outcome: | The proposed model performs better or at par with the state-of-the-art models. |
Beyond Model Performance: Can Link Prediction Enrich French Lexical Graphs? (2024.lrec-main)
Copied to clipboard
| Challenge: | lexical resources are essential for the development of NLP systems, but with advances in language models and deep learning, they are increasingly being replaced by web-derived text. |
| Approach: | They propose a resource-centric study of link prediction approaches over French lexical-semantic graphs. |
| Outcome: | The proposed method is more accurate and reliable than previous methods. |
Fine-Grained Temporal Relation Extraction (P19-1)
Copied to clipboard
| Challenge: | Existing methods for temporal relations and event durations are insufficient for determining the fine-grained temporal structure of complex events. |
| Approach: | They propose a semantic framework for temporal relations and event durations that maps pairs of events to real-valued scales. |
| Outcome: | The proposed framework can predict fine-grained temporal relations and event durations . it can be applied to the entire English Web Treebank dataset . |
Open-Domain Contextual Link Prediction and its Complementarity with Entailment Graphs (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for linking knowledge graphs only use textual contexts . contextual link prediction is useful for finding context-dependent entailments . |
| Approach: | They propose a task of open-domain contextual link prediction which uses textual context and KG structure to perform link prediction. |
| Outcome: | The proposed model can ground the triples in the context of the original dataset and infer missing relations in context. |
LPNL: Scalable Link Prediction with Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on graph learning with large language models have focused on the link prediction task on large graphs. |
| Approach: | They propose a framework for scalable link prediction on large-scale heterogeneous graphs based on large language models. |
| Outcome: | The proposed framework outperforms baselines in link prediction tasks on large graphs. |
WordNet Is All You Need: A Surprisingly Effective Unsupervised Method for Graded Lexical Entailment (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a simple unsupervised method for predicting graded lexical entailment in English relies on WordNet . despite its simplicity, our method outperforms all previous methods using WordNet as weak supervision. |
| Approach: | They propose an unsupervised method which relies exclusively on WordNet for predicting graded lexical entailment in English. |
| Outcome: | The proposed method outperforms existing methods on the largest GLE dataset using WordNet. |
Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing models of language understanding are based on explicit representations of hierarchical structure, but there are good reasons to doubt that they can be said to understand language in any meaningful way. |
| Approach: | They examine whether syntactic and semantic graph representations can complement and improve neural language modeling. |
| Outcome: | The proposed model outperforms pretrained models on English WSJ in perplexity and other metrics. |