The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction

@inproceedings{Ammar2017TheAS,
  title={The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction},
  author={Waleed Ammar and Matthew E. Peters and Chandra Bhagavatula and R. Power},
  booktitle={*SEMEVAL},
  year={2017}
}
This paper describes our submission for the ScienceIE shared task (SemEval- 2017 Task 10) on entity and relation extraction from scientific papers. Our model is based on the end-to-end relation extraction model of Miwa and Bansal (2016) with several enhancements such as semi-supervised learning via neural language models, character-level encoding, gazetteers extracted from existing knowledge bases, and model ensembles. Our official submission ranked first in end-to-end entity and relation… Expand
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References

SHOWING 1-10 OF 10 REFERENCES
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 andExpand
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
TLDR
A novel end-to-end neural model to extract entities and relations between them and compares favorably to the state-of-the-art CNN based model (in F1-score) on nominal relation classification (SemEval-2010 Task 8). Expand
Natural Language Processing (Almost) from Scratch
We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entityExpand
Exploring the Limits of Language Modeling
TLDR
This work explores recent advances in Recurrent Neural Networks for large scale Language Modeling, and extends current models to deal with two key challenges present in this task: corpora and vocabulary sizes, and complex, long term structure of language. Expand
One billion word benchmark for measuring progress in statistical language modeling
TLDR
A new benchmark corpus to be used for measuring progress in statistical language modeling, with almost one billion words of training data, is proposed, which is useful to quickly evaluate novel language modeling techniques, and to compare their contribution when combined with other advanced techniques. Expand
GloVe: Global Vectors for Word Representation
TLDR
A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure. Expand
Adam: A Method for Stochastic Optimization
TLDR
This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Expand
Long Short-Term Memory
TLDR
A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Expand
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
TLDR
This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data. Expand
Natural language processing ( almost ) from scratch . In JMLR . Sepp Hochreiter and Jürgen Schmidhuber . 1997 . Long short - term memory
  • Neural Computation
  • 2011