Building a Knowledge Graph from Natural Language Definitions for Interpretable Text Entailment Recognition (L18-1)
Copied to clipboard
| Challenge: | a conceptual model for dictionary definitions is used to construct a knowledge graph from natural language definitions. |
| Approach: | They propose a method for automatically building a graph world knowledge base from natural language definitions. |
| Outcome: | The proposed method was used in an interpretable text entailment recognition approach. |
Similar Papers
Inspecting the concept knowledge graph encoded by modern language models (2021.findings-acl)
Copied to clipboard
| Challenge: | Pre-trained language models are used to solve tasks such as summarization and information retrieval. |
| Approach: | They propose to use word embeddings, text generators, context encoders to extract underlying knowledge graphs of nine influential language models. |
| Outcome: | The proposed model is able to encode word embeddings, text generators, and context encoders, but suffers from several inaccuracies. |
Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models (N19-1)
Copied to clipboard
| Challenge: | a novel method for mapping unrestricted text to knowledge graph entities is proposed . a proof-of-concept experiment has encouraging results comparable to those of state-of the-art systems. |
| Approach: | They propose a method for mapping unrestricted text to knowledge graph entities by framing the task as a sequence-to-sequence problem. |
| Outcome: | The proposed method produces highly interpretable predictions comparable to state-of-the-art systems. |
Graph-Based Meaning Representations: Design and Processing (P19-4)
Copied to clipboard
| Challenge: | This tutorial focuses on representing and processing sentence meaning in the form of labeled directed graphs. |
| Approach: | This tutorial will briefly review relevant background in formal and linguistic semantics . it will also briefly define a unified abstract view on different flavors of semantic graphs - and associated terminology . |
| Outcome: | The tutorial will briefly review relevant background in formal and linguistic semantics . |
Storytelling from Structured Data and Knowledge Graphs : An NLG Perspective (P19-4)
Copied to clipboard
| Challenge: | tutorial aims to explain the basic concepts of translating structured data into natural language . Various solutions for structured data translation will be discussed . |
| Approach: | tutorial aims to cover foundational, methodological, and system development aspects of translating structured data into natural language . Various solutions starting from traditional rule based/heuristic driven and modern data-driven will be discussed . |
| Outcome: | The tutorial aims to convey challenges and nuances in structured data translation, data representation techniques, and domain adaptable solutions for translation of the data into natural language form. |
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for knowledge-to-text generation use RDF triples or key-value pairs to generate a natural language description. |
| Approach: | They propose a large-scale dataset to facilitate the study of KG-to-text . they propose MGCN model architecture that incorporates aggregation methods to extract the rich graph information. |
| Outcome: | The proposed model can represent the original graph information more comprehensively and integrates multiple aggregation methods to extract the rich graph information. |
Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing work on data-to-text generation focused on domain-specific benchmark datasets. |
| Approach: | They use a KG-Wikipedia text aligned corpus to verbalize the entire English Wikidata KG . they show that this approach can be used to integrate structured KGs and natural language corpora . |
| Outcome: | The proposed method improves on open domain QA and the LAMA knowledge probe. |
Automated Graph Generation at Sentence Level for Reading Comprehension Based on Conceptual Graphs (2020.coling-main)
Copied to clipboard
| Challenge: | Using conceptual graphs, we can embed a sentence into a knowledge embedding in a graph to solve slot-filling challenges in question answering and capture neighbouring connections of reference concept nodes. |
| Approach: | They propose a miscellaneous-context-based method to embed a sentence into a directed graph and a task-agnostic semantics module which integrates the syntactic-semantic information. |
| Outcome: | The proposed method can accommodate slot-filling challenges in typical question answering and capture the neighbouring connections of reference concept nodes. |
Embedding WordNet Knowledge for Textual Entailment (C18-1)
Copied to clipboard
| Challenge: | Existing deep learning models for textual entailment do not require any feature engineering or linguistic analysis. |
| Approach: | They propose to embed WordNet-derived lexical entailment relations into specially-learned word vectors and incorporate them into a decomposable attention model for textual enlightment. |
| Outcome: | The proposed model significantly improves on the SICK and SNLI datasets. |
Text Annotation Graphs: Annotating Complex Natural Language Phenomena (L18-1)
Copied to clipboard
| Challenge: | Text Annotation Graphs is a web-based tool for annotating text . it provides functionality for representing complex relationships between words and word phrases . |
| Approach: | They introduce a web-based tool for annotating text, Text Annotation Graphs, or TAG . it provides functionality for representing complex relationships between words and word phrases . |
| Outcome: | The proposed software can represent complex relationships between words and words . it can also be used to find similar structures within the current document or external annotated documents. |
Enriching Word Usage Graphs with Cluster Definitions (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing word usage graphs lack human interpretability of senses. |
| Approach: | They propose to enrich existing word usage graphs with cluster labels functioning as sense definitions. |
| Outcome: | The proposed dataset matches the definitions chosen from WordNet by two baseline systems. |