Context-Aware Representations for Knowledge Base Relation Extraction
We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation. Our architecture uses an LSTM-based encoder to jointly learn representations for all relations in a single sentence. We combine the context representations with an attention mechanism to make the final prediction. We use the Wikidata knowledge base to construct a dataset of multiple relations per sentence and to evaluate our approach. Compared to a baseline system, our method results in an average error reduction of 24 on a held-out set of relations. The code and the dataset to replicate the experiments are made available at \url{https://github.com/ukplab/}.
Code (1)
Tasks
Question AnsweringRelationRelation ExtractionSentenceSimilar Papers 제목 키워드 기반
FlexStructRAG: Flexible Structure-Aware Multi-Granular Relational Retrieval for RAG
Retrieval-Augmented Generation (RAG) systems critically depend on how external knowledge is segmented, structured, and retrieved. Most existing approaches either retrieve fixed-length text chunks, which fragments discour…
Comprehensive Event Representations using Event Knowledge Graphs and Natural Language Processing
Recent work has utilised knowledge-aware approaches to natural language understanding, question answering, recommendation systems, and other tasks. These approaches rely on well-constructed and large-scale knowledge grap…
Event ExtractionKnowledge Graph CompletionKnowledge GraphsNatural Language Understanding+3Context-Aware Embeddings for Automatic Art Analysis
Automatic art analysis aims to classify and retrieve artistic representations from a collection of images by using computer vision and machine learning techniques. In this work, we propose to enhance visual representatio…
Art AnalysisCross-Modal RetrievalGeneral ClassificationMulti-Task Learning+1A Unified Framework for Context-Aware and Relation-Aware Graph Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) has emerged as a paradigm for enhancing large language models (LLMs) with external knowledge, yet existing graph-based methods face a fundamental limitation: entity-centric and chunk-…
PVLR: Prompt-driven Visual-Linguistic Representation Learning for Multi-Label Image Recognition
Multi-label image recognition is a fundamental task in computer vision. Recently, vision-language models have made notable advancements in this area. However, previous methods often failed to effectively leverage the ric…
Multi-Label Image RecognitionRepresentation Learning