Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications

Vivi Nastase, Benjamin Roth, Laura Dietz, Andrew McCallum (Editors)


Anthology ID:
W19-26
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
URL:
https://aclanthology.org/W19-26
DOI:
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PDF:
https://aclanthology.org/W19-26.pdf

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Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications
Vivi Nastase | Benjamin Roth | Laura Dietz | Andrew McCallum

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Distantly Supervised Biomedical Knowledge Acquisition via Knowledge Graph Based Attention
Qin Dai | Naoya Inoue | Paul Reisert | Ryo Takahashi | Kentaro Inui

The increased demand for structured scientific knowledge has attracted considerable attention in extracting scientific relation from the ever growing scientific publications. Distant supervision is widely applied approach to automatically generate large amounts of labelled data with low manual annotation cost. However, distant supervision inevitably accompanies the wrong labelling problem, which will negatively affect the performance of Relation Extraction (RE). To address this issue, (Han et al., 2018) proposes a novel framework for jointly training both RE model and Knowledge Graph Completion (KGC) model to extract structured knowledge from non-scientific dataset. In this work, we firstly investigate the feasibility of this framework on scientific dataset, specifically on biomedical dataset. Secondly, to achieve better performance on the biomedical dataset, we extend the framework with other competitive KGC models. Moreover, we proposed a new end-to-end KGC model to extend the framework. Experimental results not only show the feasibility of the framework on the biomedical dataset, but also indicate the effectiveness of our extensions, because our extended model achieves significant and consistent improvements on distant supervised RE as compared with baselines.

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Understanding the Polarity of Events in the Biomedical Literature : Deep Learning vs. Linguistically-informed Methods
Enrique Noriega-Atala | Zhengzhong Liang | John Bachman | Clayton Morrison | Mihai Surdeanu

An important task in the machine reading of biochemical events expressed in biomedical texts is correctly reading the polarity, i.e., attributing whether the biochemical event is a promotion or an inhibition. Here we present a novel dataset for studying polarity attribution accuracy. We use this dataset to train and evaluate several deep learning models for polarity identification, and compare these to a linguistically-informed model. The best performing deep learning architecture achieves 0.968 average F1 performance in a five-fold cross-validation study, a considerable improvement over the linguistically informed model average F1 of 0.862.

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Dataset Mention Extraction and Classification
Animesh Prasad | Chenglei Si | Min-Yen Kan

Datasets are integral artifacts of empirical scientific research. However, due to natural language variation, their recognition can be difficult and even when identified, can often be inconsistently referred across and within publications. We report our approach to the Coleridge Initiative’s Rich Context Competition, which tasks participants with identifying dataset surface forms (dataset mention extraction) and associating the extracted mention to its referred dataset (dataset classification). In this work, we propose various neural baselines and evaluate these model on one-plus and zero-shot classification scenarios. We further explore various joint learning approaches-exploring the synergy between the tasks-and report the issues with such techniques.

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Annotating with Pros and Cons of Technologies in Computer Science Papers
Hono Shirai | Naoya Inoue | Jun Suzuki | Kentaro Inui

This paper explores a task for extracting a technological expression and its pros / cons from computer science papers. We report ongoing efforts on an annotated corpus of pros / cons and an analysis of the nature of the automatic extraction task. Specifically, we show how to adapt the targeted sentiment analysis task for pros / cons extraction in computer science papers and conduct an annotation study. In order to identify the challenges of the automatic extraction task, we construct a strong baseline model and conduct an error analysis. The experiments show that pros / cons can be consistently annotated by several annotators, and that the task is challenging due to domain-specific knowledge. The annotated dataset is made publicly available for research purposes.

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An Analysis of Deep Contextual Word Embeddings and Neural Architectures for Toponym Mention Detection in Scientific Publications
Matthew Magnusson | Laura Dietz

Toponym detection in scientific papers is an open task and a key first step in place entity enrichment of documents. We examine three common neural architectures in NLP : 1) convolutional neural network, 2) multi-layer perceptron (both applied in a sliding window context) and 3) bidirectional LSTM and apply contextual and non-contextual word embedding layers to these models. We find that deep contextual word embeddings improve the performance of the bi-LSTM with CRF neural architecture achieving the best performance when multiple layers of deep contextual embeddings are concatenated. Our best performing model achieves an average F1 of 0.910 when evaluated on overlap macro exceeding previous state-of-the-art models in the toponym detection task.