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Joint Event Extraction via Structured Prediction with Global Features
TLDR
This work proposes a joint framework based on structured prediction which extracts triggers and arguments together so that the local predictions can be mutually improved, and proposes to incorporate global features which explicitly capture the dependencies of multiple triggers and argued.
Overview of the TAC 2010 Knowledge Base Population Track
TLDR
An overview of the task definition and annotation challenges associated with KBP2010 is provided and the evaluation results and lessons that are learned are discussed based on detailed analysis.
Cross-lingual Name Tagging and Linking for 282 Languages
TLDR
This work develops a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia that is able to identify name mentions, assign a coarse-grained or fine- grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable.
Incremental Joint Extraction of Entity Mentions and Relations
TLDR
An incremental joint framework to simultaneously extract entity mentions and relations using structured perceptron with efficient beam-search is presented, which significantly outperforms a strong pipelined baseline, which attains better performance than the best-reported end-to-end system.
AFET: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label Embedding
TLDR
This paper proposes a novel embedding method to separately model “clean” and “noisy” mentions, and incorporates the given type hierarchy to induce loss functions.
Knowledge Base Population: Successful Approaches and Challenges
TLDR
The techniques which can serve as a basis for a good KBP system are provided, the remaining challenges by comparison with traditional Information Extraction (IE) and Question Answering (QA) tasks are laid out, and some suggestions to address these challenges are provided.
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
TLDR
A novel domain-independent framework that jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces, and adopts a novel partial-label loss function for noisy labeled data and introduces an object "translation" function to capture the cross-constraints of entities and relations on each other.
Overview of TAC-KBP2015 Tri-lingual Entity Discovery and Linking
TLDR
An overview of the task definition, annotation issues, successful methods and research challenges associated with this new end-to-end Tri-lingual entity discovery and linking task at the Knowledge Base Population (KBP) track at TAC2015 is given.
A Dependency-Based Neural Network for Relation Classification
TLDR
A new structure, termed augmented dependency path (ADP), is proposed, which is composed of the shortest dependency path between two entities and the subtrees attached to the shortest path, and a dependency-based neural networks (DepNN) are developed to exploit the semantic representation behind the ADP.
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
TLDR
A global objective is formulated for learning the embeddings from text corpora and knowledge bases, which adopts a novel margin-based loss that is robust to noisy labels and faithfully models type correlation derived from knowledge bases.
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