• Corpus ID: 14344787

Extracting Multilingual Relations under Limited Resources: TAC 2016 Cold-Start KB construction and Slot-Filling using Compositional Universal Schema

@article{Chang2016ExtractingMR,
  title={Extracting Multilingual Relations under Limited Resources: TAC 2016 Cold-Start KB construction and Slot-Filling using Compositional Universal Schema},
  author={Haw-Shiuan Chang and Abdurrahman Munir and Ao Liu and Johnny Tian-Zheng Wei and Aaron Traylor and Ajay Nagesh and Nicholas Monath and Pat Verga and Emma Strubell and Andrew McCallum},
  journal={Theory and Applications of Categories},
  year={2016}
}
We describe the UMass IESL relation extraction system for TAC KBP 2016. One of the main challenges in TAC 2016 is to extract relations from multiple languages, including those with relatively low resources like Spanish. To mitigate the problem, we integrate multilingual and compositional universal schema from Verga et al. (2016) into our slot filling and knowledge base construction pipelines. The flexibility of our universal schema framework allows us to extract high quality Spanish relations… 

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References

SHOWING 1-10 OF 18 REFERENCES

Multilingual Relation Extraction using Compositional Universal Schema

This paper's model outperforms the top system from TAC 2013 slot-filling using no handwritten patterns or additional annotation, and is suited to broad-coverage automated knowledge base construction in a variety of languages and domains.

Relation Extraction with Matrix Factorization and Universal Schemas

This work presents matrix factorization models that learn latent feature vectors for entity tuples and relations that achieve substantially higher accuracy than a traditional classification approach and is able to reason about unstructured and structured data in mutually-supporting ways.

Lexicon Infused Phrase Embeddings for Named Entity Resolution

A new form of learning word embeddings that can leverage information from relevant lexicons to improve the representations, and the first system to use neural word embedDings to achieve state-of-the-art results on named-entity recognition in both CoNLL and Ontonotes NER are presented.

Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation

A novel embedding method specifically designed for NED that jointly maps words and entities into the same continuous vector space and extends the skip-gram model by using two models.

Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks

This work presents a model that uses convolutional neural networks to capture semantic correspondence between a mention's context and a proposed target entity, outperforming the prior systems of Durrett and Klein (2014) and Nguyen et al. (2014).

Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation

A novel deep semantic relatedness model (DSRM) based on deep neural networks (DNN) and semantic knowledge graphs (KGs) to measure entity semantic relatednesses for topical coherence modeling is presented.

RelationFactory: A Fast, Modular and Effective System for Knowledge Base Population

RelationFactory emphasizes modularity, is easily configurable and uses a transparent pipelined approach and a recall error analysis component categorizes and illustrates cases in which the system missed a correct answer.

Comparing Convolutional Neural Networks to Traditional Models for Slot Filling

A convolutional neural network is proposed which splits the input sentence into three parts according to the relation arguments and is compared to state-ofthe-art and traditional approaches of relation classification.

Modeling Mention, Context and Entity with Neural Networks for Entity Disambiguation

A new neural network approach is presented that takes consideration of the semantic representations of mention, context and entity, encodes them in continuous vector space and effectively leverages them for entity disambiguation.

Joint Learning of Local and Global Features for Entity Linking via Neural Networks

This work introduces a novel framework based on convolutional neural networks and recurrent neural networks to simultaneously model the local and global features for entity linking and examines the entity linking systems on the domain adaptation setting that further demonstrates the cross-domain robustness of the proposed model.