Enriching BERT with Knowledge Graph Embeddings for Document Classification
In this paper, we focus on the classification of books using short descriptive texts (cover blurbs) and additional metadata. Building upon BERT, a deep neural language model, we demonstrate how to combine text representations with metadata and knowledge graph embeddings, which encode author information. Compared to the standard BERT approach we achieve considerably better results for the classification task. For a more coarse-grained classification using eight labels we achieve an F1- score of 87.20, while a detailed classification using 343 labels yields an F1-score of 64.70. We make the source code and trained models of our experiments publicly available
Code (1)
Tasks
ClassificationDescriptiveDocument ClassificationGeneral ClassificationKnowledge Graph EmbeddingsLanguage ModelingLanguage ModellingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
TinySearch -- Semantics based Search Engine using Bert Embeddings
Existing search engines use keyword matching or tf-idf based matching to map the query to the web-documents and rank them. They also consider other factors such as page rank, hubs-and-authority scores, knowledge graphs t…
Knowledge GraphsAn Exploitation of Heterogeneous Graph Neural Network for Extractive Long Document Summarization
Heterogeneous Graph Neural Networks (HeterGNN) has been recently introduced as an emergent approach for many Natural Language Processing (NLP) tasks by enriching the complex information between word and sentence. In this…
Document SummarizationExtractive Document SummarizationGraph AttentionGraph Neural Network+1Topic Aware Contextualized Embeddings for High Quality Phrase Extraction
Keyphrase extraction from a given document is the task of automatically extracting salient phrases that best describe the document. This paper proposes a novel unsupervised graph-based ranking method to extract high-qual…
Keyphrase ExtractionVocal Bursts Intensity PredictionEnriching Epidemiological Thematic Features For Disease Surveillance Corpora Classification
We present EpidBioBERT, a biosurveillance epidemiological document tagger for disease surveillance over PADI-Web system. Our model is trained on PADI-Web corpus which contains news articles on Animal Diseases Outbreak ex…
ArticlesClassificationDocument ClassificationEpidemiology+2ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification
Graph neural networks (GNNs) have been recently applied in natural language processing. Various GNN research studies are proposed to learn node interactions within the local graph of each document that contains words, se…
text-classificationText ClassificationWord Embeddings