Knowledge Base Population
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Greatest papers with code
Position-aware Attention and Supervised Data Improve Slot Filling
EMNLP 2017
• yuhaozhang/tacred-relation
• 
The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance.
#11 best model for
Relation Extraction
on TACRED
KNOWLEDGE BASE POPULATION KNOWLEDGE GRAPHS RELATION EXTRACTION SLOT FILLING
Entity Disambiguation with Web Links
Entity disambiguation with Wikipedia relies on structured information from redirect pages, article text, inter-article links, and categories.
ENTITY DISAMBIGUATION ENTITY LINKING KNOWLEDGE BASE POPULATION
KnowledgeNet: A Benchmark Dataset for Knowledge Base Population
KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web.
ENTITY LINKING KNOWLEDGE BASE POPULATION RELATION EXTRACTION
SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications
We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials.
Discovering Implicit Knowledge with Unary Relations
State-of-the-art relation extraction approaches are only able to recognize relationships between mentions of entity arguments stated explicitly in the text and typically localized to the same sentence.
KNOWLEDGE BASE POPULATION NATURAL LANGUAGE INFERENCE RELATION EXTRACTION
Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags
In this work, we propose a model which alleviates the need for such disambiguators by jointly learning NER and MD taggers in languages for which one can provide a list of candidate morphological analyses.
ENTITY LINKING KNOWLEDGE BASE POPULATION NAMED ENTITY RECOGNITION RELATION EXTRACTION WORD EMBEDDINGS
Adversarial Feature Adaptation for Cross-lingual Relation Classification
COLING 2018
• zoubowei/feature_adaptation4RC
•
In this paper, we come up with a feature adaptation approach for cross-lingual relation classification, which employs a generative adversarial network (GAN) to transfer feature representations from one language with rich annotated data to another language with scarce annotated data.
DOMAIN ADAPTATION KNOWLEDGE BASE POPULATION QUESTION ANSWERING RELATION CLASSIFICATION REPRESENTATION LEARNING