Proceedings of the Third Workshop on Structured Prediction for NLP

Andre Martins, Andreas Vlachos, Zornitsa Kozareva, Sujith Ravi, Gerasimos Lampouras, Vlad Niculae, Julia Kreutzer (Editors)


Anthology ID:
W19-15
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
URL:
https://aclanthology.org/W19-15
DOI:
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PDF:
https://aclanthology.org/W19-15.pdf

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Proceedings of the Third Workshop on Structured Prediction for NLP
Andre Martins | Andreas Vlachos | Zornitsa Kozareva | Sujith Ravi | Gerasimos Lampouras | Vlad Niculae | Julia Kreutzer

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Lightly-supervised Representation Learning with Global Interpretability
Andrew Zupon | Maria Alexeeva | Marco Valenzuela-Esc√°rcega | Ajay Nagesh | Mihai Surdeanu

We propose a lightly-supervised approach for information extraction, in particular named entity classification, which combines the benefits of traditional bootstrapping, i.e., use of limited annotations and interpretability of extraction patterns, with the robust learning approaches proposed in representation learning. Our algorithm iteratively learns custom embeddings for both the multi-word entities to be extracted and the patterns that match them from a few example entities per category. We demonstrate that this representation-based approach outperforms three other state-of-the-art bootstrapping approaches on two datasets : CoNLL-2003 and OntoNotes. Additionally, using these embeddings, our approach outputs a globally-interpretable model consisting of a decision list, by ranking patterns based on their proximity to the average entity embedding in a given class. We show that this interpretable model performs close to our complete bootstrapping model, proving that representation learning can be used to produce interpretable models with small loss in performance. This decision list can be edited by human experts to mitigate some of that loss and in some cases outperform the original model.

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Semi-Supervised Teacher-Student Architecture for Relation Extraction
Fan Luo | Ajay Nagesh | Rebecca Sharp | Mihai Surdeanu

Generating a large amount of training data for information extraction (IE) is either costly (if annotations are created manually), or runs the risk of introducing noisy instances (if distant supervision is used). On the other hand, semi-supervised learning (SSL) is a cost-efficient solution to combat lack of training data. In this paper, we adapt Mean Teacher (Tarvainen and Valpola, 2017), a denoising SSL framework to extract semantic relations between pairs of entities. We explore the sweet spot of amount of supervision required for good performance on this binary relation extraction task. Additionally, different syntax representations are incorporated into our models to enhance the learned representation of sentences. We evaluate our approach on the Google-IISc Distant Supervision (GDS) dataset, which removes test data noise present in all previous distance supervision datasets, which makes it a reliable evaluation benchmark (Jat et al., 2017). Our results show that the SSL Mean Teacher approach nears the performance of fully-supervised approaches even with only 10 % of the labeled corpus. Further, the syntax-aware model outperforms other syntax-free approaches across all levels of supervision.